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INTRODUCTION: Recent technological advances have increased the risk that de-identified brain images could be re-identified from face imagery. The Alzheimers Disease Neuroimaging Initiative (ADNI) is a leading source of publicly available de-identified brain imaging, who quickly acted to protect participants privacy. METHODS: An independent expert committee evaluated 11 face-deidentification (de-facing) methods and selected four for formal testing. RESULTS: Effects of de-facing on brain measurements were comparable across methods and sufficiently small to recommend de-facing in ADNI. The committee ultimately recommended mri_reface for advantages in reliability, and for some practical considerations. ADNI leadership approved the committees recommendation, beginning in ADNI4. DISCUSSION: ADNI4 de-faces all applicable brain images before subsequent pre-processing, analyses, and public release. Trained analysts inspect de-faced images to confirm complete face removal and complete non-modification of brain. This paper details the history of the algorithm selection process and extensive validation, then describes the production workflows for de-facing in ADNI. HIGHLIGHTS: ADNI is implementing de-facing of MRI and PET beginning in ADNI4. De-facing alters face imagery in brain images to help protect privacy. Four algorithms were extensively compared for ADNI and mri_reface was chosen. Validation confirms mri_reface is robust and effective for ADNI sequences. Validation confirms mri_reface negligibly affects ADNI brain measurements.

Abstract A large number of images are available on the Internet with the growth of social networking services, and many of them are face photos or contain faces. It is necessary to protect the privacy of face images to prevent their malicious use by face image de-identification techniques that make face recognition difficult, which prevent the collection of specific face images using face recognition. In this paper, we propose a face image de-identification method that generates a de-identified image from an input face image by embedding facial features extracted from that of another person into the input face image. We develop the novel framework for embedding facial features into a face image and loss functions based on images and features to de-identify a face image preserving its appearance. Through a set of experiments using public face image datasets, we demonstrate that the proposed method exhibits higher de-identification performance against unknown face recognition models than conventional methods while preserving the appearance of the input face images.

A large number of images are available on the Internet with the growth of social networking services, and many of them are face photos or contain faces. It is necessary to protect the privacy of face images to prevent their malicious use by face image de-identification techniques that make face recognition difficult, which prevent the collection of specific face images using face recognition. In this paper, we propose a face image de-identification method that generates a de-identified image from an input face image by embedding facial features extracted from that of another person into the input face image. We develop the novel framework for embedding facial features into a face image and loss functions based on images and features to de-identify a face image preserving its appearance. Through a set of experiments using public face image datasets, we demonstrate that the proposed method exhibits higher de-identification performance against unknown face recognition models than conventional methods while preserving the appearance of the input face images. [ABSTRACT FROM AUTHOR]

License plate (LP) information is an important part of personal privacy, which is protected by law. However, in some publicly available transportation datasets, the LP areas in the images have not been processed. Other datasets have applied simple de-identification operations such as blurring and masking. Such crude operations will lead to a reduction in data utility. In this paper, we propose a method of LP de-identification based on a generative adversarial network (LPDi GAN) to transform an original image to a synthetic one with a generated LP. To maintain the original LP attributes, the background features are extracted from the background to generate LPs that are similar to the originals. The LP template and LP style are also fed into the network to obtain synthetic LPs with controllable characters and higher quality. The results show that LPDi GAN can perceive changes in environmental conditions and LP tilt angles, and control the LP characters through the LP templates. The perceptual similarity metric, Learned Perceptual Image Patch Similarity (LPIPS), reaches 0.25 while ensuring the effect of character recognition on de-identified images, demonstrating that LPDi GAN can achieve outstanding de-identification while preserving strong data utility. [ABSTRACT FROM AUTHOR]

Background: The recent rise of large language models has triggered renewed interest in medical free text data, which holds critical information about patients and diseases. However, medical free text is also highly sensitive. Therefore, de-identification is typically required but is complicated since medical free text is mostly unstructured. With the Masketeer algorithm, we present an effective tool to de-identify German medical text. Methods: We used an ensemble of different masking classes to remove references to identifiable data from over 35,000 clinical notes in accordance with the HIPAA Safe Harbor Guidelines. To retain additional context for readers, we implemented an entity recognition scheme and corpus-wide pseudonymization. Results: The algorithm performed with a sensitivity of 0.943 and specificity of 0.933. Further performance analyses showed linear runtime complexity (O(n)) with both increasing text length and corpus size. Conclusions: In the future, large language models will likely be able to de-identify medical free text more effectively and thoroughly than handcrafted rules. However, such gold-standard de-identification tools based on large language models are yet to emerge. In the current absence of such, we hope to provide best practices for a robust rule-based algorithm designed with expert domain knowledge. [ABSTRACT FROM AUTHOR]

ObjectivesClinical notes are a veritable treasure trove of information on a patient's disease progression, medical history, and treatment plans, yet are locked in secured databases accessible for research only after extensive ethics review. Removing personally identifying and protected health information (PII/PHI) from the records can reduce the need for additional Institutional Review Boards (IRB) reviews. In this project, our goals were to: (1) develop a robust and scalable clinical text de-identification pipeline that is compliant with the Health Insurance Portability and Accountability Act (HIPAA) Privacy Rule for de-identification standards and (2) share routinely updated de-identified clinical notes with researchers.Materials and methodsBuilding on our open-source de-identification software called Philter, we added features to: (1) make the algorithm and the de-identified data HIPAA compliant, which also implies type 2 error-free redaction, as certified via external audit; (2) reduce over-redaction errors; and (3) normalize and shift date PHI. We also established a streamlined de-identification pipeline using MongoDB to automatically extract clinical notes and provide truly de-identified notes to researchers with periodic monthly refreshes at our institution.ResultsTo the best of our knowledge, the Philter V1.0 pipeline is currently the first and only certified, de-identified redaction pipeline that makes clinical notes available to researchers for nonhuman subjects' research, without further IRB approval needed. To date, we have made over 130 million certified de-identified clinical notes available to over 600 UCSF researchers. These notes were collected over the past 40 years, and represent data from 2757016 UCSF patients.

Respect for research participant rights is a key aspect for consideration when creating and utilizing interoperable data. From that perspective, requirements for sharing research data often call for the data to be de-identified, i.e., the removal of all personal identifying information (PII) prior to data sharing, to ensure that the participant’s data privacy rights are not infringed upon. However, what constitutes PII is often a point of confusion amongst researchers who are not familiar with privacy laws and regulations. This paper hopes to provide some clarity around what makes research data identifiable by presenting it under a different perspective from what most researchers are familiar with. It also provides a framework to help researchers determine where PII could exist within their data that they can use to help with privacy impact evaluations. The goal is to empower researchers to share their data with greater confidence that the privacy rights of their research subjects have been sufficiently protected, enabling access to greater amounts of data for research use.

Abstract Many state-of-the-art results in natural language processing (NLP) rely on large pre-trained language models (PLMs). These models consist of large amounts of parameters that are tuned using vast amounts of training data. These factors cause the models to memorize parts of their training data, making them vulnerable to various privacy attacks. This is cause for concern, especially when these models are applied in the clinical domain, where data are very sensitive. Training data pseudonymization is a privacy-preserving technique that aims to mitigate these problems. This technique automatically identifies and replaces sensitive entities with realistic but non-sensitive surrogates. Pseudonymization has yielded promising results in previous studies. However, no previous study has applied pseudonymization to both the pre-training data of PLMs and the fine-tuning data used to solve clinical NLP tasks. This study evaluates the effects on the predictive performance of end-to-end pseudonymization of Swedish clinical BERT models fine-tuned for five clinical NLP tasks. A large number of statistical tests are performed, revealing minimal harm to performance when using pseudonymized fine-tuning data. The results also find no deterioration from end-to-end pseudonymization of pre-training and fine-tuning data. These results demonstrate that pseudonymizing training data to reduce privacy risks can be done without harming data utility for training PLMs.

Many state-of-the-art results in natural language processing (NLP) rely on large pre-trained language models (PLMs). These models consist of large amounts of parameters that are tuned using vast amounts of training data. These factors cause the models to memorize parts of their training data, making them vulnerable to various privacy attacks. This is cause for concern, especially when these models are applied in the clinical domain, where data are very sensitive. Training data pseudonymization is a privacy-preserving technique that aims to mitigate these problems. This technique automatically identifies and replaces sensitive entities with realistic but non-sensitive surrogates. Pseudonymization has yielded promising results in previous studies. However, no previous study has applied pseudonymization to both the pre-training data of PLMs and the fine-tuning data used to solve clinical NLP tasks. This study evaluates the effects on the predictive performance of end-to-end pseudonymization of Swedish clinical BERT models fine-tuned for five clinical NLP tasks. A large number of statistical tests are performed, revealing minimal harm to performance when using pseudonymized fine-tuning data. The results also find no deterioration from end-to-end pseudonymization of pre-training and fine-tuning data. These results demonstrate that pseudonymizing training data to reduce privacy risks can be done without harming data utility for training PLMs. [ABSTRACT FROM AUTHOR]

With the rise of open data, identifiability of individuals based on 3D renderings obtained from routine structural magnetic resonance imaging (MRI) scans of the head has become a growing privacy concern. To protect subject privacy, several algorithms have been developed to de‐identify imaging data using blurring, defacing or refacing. Completely removing facial structures provides the best re‐identification protection but can significantly impact post‐processing steps, like brain morphometry. As an alternative, refacing methods that replace individual facial structures with generic templates have a lower effect on the geometry and intensity distribution of original scans, and are able to provide more consistent post‐processing results by the price of higher re‐identification risk and computational complexity. In the current study, we propose a novel method for anonymized face generation for defaced 3D T1‐weighted scans based on a 3D conditional generative adversarial network. To evaluate the performance of the proposed de‐identification tool, a comparative study was conducted between several existing defacing and refacing tools, with two different segmentation algorithms (FAST and Morphobox). The aim was to evaluate (i) impact on brain morphometry reproducibility, (ii) re‐identification risk, (iii) balance between (i) and (ii), and (iv) the processing time. The proposed method takes 9 s for face generation and is suitable for recovering consistent post‐processing results after defacing. [ABSTRACT FROM AUTHOR]

Medical research plays a crucial role within scientific research. Technological advancements, especially those related to the rise of machine learning, pave the way for the exploration of medical issues that were once beyond reach. Unstructured textual data, such as correspondence between doctors, operative reports, etc., often serve as a starting point for many medical applications.However, for obvious privacy reasons, researchers do not legally have the right to access these documents as long as they contain sensitive data, as defined by regulations like GDPR (General Data Protection Regulation) or HIPAA (Health Insurance Portability and Accountability Act). De-identification, meaning the detection, removal or substitution of all sensitive information, is therefore a necessary step to facilitate the sharing of these data between the medical field and research. Over the past decade, various approaches have been proposed to de-identify medical textual data. However, while entity detection is a well-known task in the natural language processing field, it presents some specific challenges in the medical context. Moreover, existing substitution methods proposed in the literature often pay little attention to the medical relevance of de-identified data or are not very resilient to attacks.This paper addresses these challenges. Firstly, an efficient system for detecting sensitive entities in French medical data and then accurately substitute them was implemented. Secondly, robust strategies for generating substitutes that incorporate the medical utility of the data were provided, thereby minimizing the difference in utility between the original and de-identified data, and that mathematically ensure privacy protection. Thirdly, the utility of the de-identification system in a context of ICD-10 code association was evaluated. Finally, various systems developed to tackle ICD-10 code association were presented while providing a state-of-the-art model in French.

The paper aims to evaluate and provide a comparative analysis of the performance and fine-tuning cost of various Large Language Models (LLMs) such as GPT-3.5, GPT-4, PaLM, Bard, and Llama in automating the de-identification of Protected Health Information (PHI) from medical records, ensuring patient and healthcare professional privacy. Zero-shot learning was utilized initially to assess the capabilities of these LLMs in de-identifying medical data. Subsequently, each model was fine-tuned with varying training set sizes to observe changes in performance. The study also investigates the impact of the specificity of prompts on the accuracy of de-identification tasks. Fine-tuning LLMs with specific examples significantly enhanced the accuracy of the de-identification process, surpassing the zero-shot learning accuracy of pre-trained counterparts. Notably, a fine-tuned GPT-3.5 model with a few-shot learning technique was able to exceed the performance of a zero-shot learning GPT-4 model, with 99% accuracy. Detailed prompts resulted in higher task accuracy across all models, yet fine-tuned models with brief instructions still outperformed pre-trained models given detailed prompts. Also, the fine-tuned models were more resilient to medical record format change than the zero-shot models. Code, calculations, and comparisons are available at https://github.com/YashwanthYS/De-Identification-of-medical-Records. The findings underscore the potential of LLMs, particularly when fine-tuned, to effectively automate the de-identification of PHI in medical records. The study highlights the importance of model training and prompt specificity in achieving high accuracy in de-identification tasks.

Safeguarding personal identity in various surveillance videos, dashcams, and on-street videos is crucial. One way to do this is to detect faces and blur them, but a better solution is to replace them with non-existent ones to maintain the naturalness of the videos. While face swapping methods have already been used in the media industry with high-quality faces, it is challenging to apply them for identity protection to faces in-the-wild where faces are often occluded and of low-resolution. Therefore, we propose a new framework for face swapping specifically designed to work with face images taken in real-world scenarios, making it useful as a privacy protection method. To tackle the issue of low-resolution images, we introduce a Cross-Resolution Contrastive Loss (CRCL) technique, which allows our neural network model to be trained using triplets of varying resolutions. This enables the model to learn and use identity information across different resolutions, thereby improving its accuracy. We also propose a plug-and-play framework that can be easily applied to existing face swapping models to handle occlusions. By explicit swapping of facial features and filling of occluded regions, our framework provides a more seamless blend. To demonstrate the effectiveness of our method in handling faces in-the-wild, we create an occluded VGGFace2 dataset consisting of face images augmented with various facial masks and hand occlusions. Through quantitative and qualitative assessments on this dataset, our proposed method demonstrates robust performance under low-resolution or occluded scenarios. Significant improvements are made in the quality of swapped faces while preserving their identity and attributes, highlighting the effectiveness of our framework in advancing face swapping as a reliable privacy protection measure.

Along with the creation of medical profiles of patients, Electronic Health Records have several secondary missions, such as health economy and research. The recent, increasing adoption of a common standard, i.e., the Fast Healthcare Interoperability Resources (FHIR), makes it easier to exchange medical data among the several parties involved, for example, in an epidemiological research activity. However, this exchange process is hindered by regulatory frameworks due to privacy issues related to the presence of personal information, which allows patients to be identified directly (or indirectly) from their medical data. When properly used, de-identification techniques can provide crucial support in overcoming these problems. FHIR-DIET aims to bring flexibility and concreteness to the implementation of de-identification of health data, supporting many customised data-processing behaviours that can be easily configured and tailored to match specific use case requirements. Our solution enables faster and easier cooperation between legal and IT professionals to establish and implement de-identification rules. The performance evaluation demonstrates the viability of processing hundreds of FHIR patient information data per second using standard hardware. We believe FHIR-DIET can be a valuable tool to satisfy the current regulation requirements and help to create added-value for the secondary use of healthcare data.

The proliferation of media-sharing platforms has led to issues with illegally edited content and the distribution of pornography. To protect personal information, de-identification technologies are being developed to prevent facial identification. Existing de-identification methods directly alter the pixel values in the face region, leading to reduced feature representation and identification accuracy. This study aims to develop a method that minimizes the possibility of personal identification while effectively preserving important features for image- and video-copy-detection tasks, proposing a new deep-learning-based de-identification approach that surpasses traditional pixel-based alteration methods. We introduce two de-identification models using different approaches: one emphasizing the contours of the original face through feature inversion and the other generating a blurred version of the face using D2GAN (Dual Discriminator Generative Adversarial Network). Both models were evaluated on their performance in image- and video-copy-detection tasks before and after de-identification, demonstrating effective feature preservation. This research presents new possibilities for personal-information protection and digital-content security, contributing to digital-rights management and law enforcement. [ABSTRACT FROM AUTHOR]

With the development of artificial intelligence (AI) technologies, biomedical imaging data play an important role in scientific research and clinical application, but the available resources are limited. Here we present Open Biomedical Imaging Archive (OBIA), a repository for archiving biomedical imaging and related clinical data. OBIA adopts five data objects (Collection, Individual, Study, Series, and Image) for data organization, and accepts the submission of biomedical images of multiple modalities, organs, and diseases. In order to protect personal privacy, OBIA has formulated a unified de-identification and quality control process. In addition, OBIA provides friendly and intuitive web interfaces for data submission, browsing, and retrieval, as well as image retrieval. As of September 2023, OBIA has housed data for a total of 937 individuals, 4136 studies, 24,701 series, and 1,938,309 images covering 9 modalities and 30 anatomical sites. Collectively, OBIA provides a reliable platform for biomedical imaging data management and offers free open access to all publicly available data to support research activities throughout the world. OBIA can be accessed at https://ngdc.cncb.ac.cn/obia.

Background: The recent rise of large language models has triggered renewed interest in medical free text data, which holds critical information about patients and diseases. However, medical free text is also highly sensitive. Therefore, de-identification is typically required but is complicated since medical free text is mostly unstructured. With the Masketeer algorithm, we present an effective tool to de-identify German medical text. Methods: We used an ensemble of different masking classes to remove references to identifiable data from over 35,000 clinical notes in accordance with the HIPAA Safe Harbor Guidelines. To retain additional context for readers, we implemented an entity recognition scheme and corpus-wide pseudonymization. Results: The algorithm performed with a sensitivity of 0.943 and specificity of 0.933. Further performance analyses showed linear runtime complexity (O(n)) with both increasing text length and corpus size. Conclusions: In the future, large language models will likely be able to de-identify medical free text more effectively and thoroughly than handcrafted rules. However, such gold-standard de-identification tools based on large language models are yet to emerge. In the current absence of such, we hope to provide best practices for a robust rule-based algorithm designed with expert domain knowledge.

License plate (LP) information is an important part of personal privacy, which is protected by law. However, in some publicly available transportation datasets, the LP areas in the images have not been processed. Other datasets have applied simple de-identification operations such as blurring and masking. Such crude operations will lead to a reduction in data utility. In this paper, we propose a method of LP de-identification based on a generative adversarial network (LPDi GAN) to transform an original image to a synthetic one with a generated LP. To maintain the original LP attributes, the background features are extracted from the background to generate LPs that are similar to the originals. The LP template and LP style are also fed into the network to obtain synthetic LPs with controllable characters and higher quality. The results show that LPDi GAN can perceive changes in environmental conditions and LP tilt angles, and control the LP characters through the LP templates. The perceptual similarity metric, Learned Perceptual Image Patch Similarity (LPIPS), reaches 0.25 while ensuring the effect of character recognition on de-identified images, demonstrating that LPDi GAN can achieve outstanding de-identification while preserving strong data utility.

Identity disclosure risk is an essential consideration in data anonymization aimed at preserving privacy and utility. The risk is regionally dependent. Therefore, there is a need for a regional empirical approach in addition to a theoretical approach in modeling disclosure risk estimation. Reviewed literature pointed to three influencers of the risk. However, we did not find literature on the combined effects of the three influencers and their predictive power. To fill the gap, this study modeled the risk estimation predicated on the combined effect of the three predictors using the Kenyan situation. The study validated the model by conducting an actual re-identification quasi-experiment. The adversary’s analytical competence, distinguishing power of the anonymized datasets, and linkage mapping of the identified datasets are presented as the predictors of the risk estimation. For each predictor, manifest variables are presented. Our presented model extends previous models and is capable of producing a realistic risk estimation. [ABSTRACT FROM AUTHOR]

Abstract Background Epidemiological research may require linkage of information from multiple organizations. This can bring two problems: (1) the information governance desirability of linkage without sharing direct identifiers, and (2) a requirement to link databases without a common person-unique identifier. Methods We develop a Bayesian matching technique to solve both. We provide an open-source software implementation capable of de-identified probabilistic matching despite discrepancies, via fuzzy representations and complete mismatches, plus de-identified deterministic matching if required. We validate the technique by testing linkage between multiple medical records systems in a UK National Health Service Trust, examining the effects of decision thresholds on linkage accuracy. We report demographic factors associated with correct linkage. Results The system supports dates of birth (DOBs), forenames, surnames, three-state gender, and UK postcodes. Fuzzy representations are supported for all except gender, and there is support for additional transformations, such as accent misrepresentation, variation for multi-part surnames, and name re-ordering. Calculated log odds predicted a proband’s presence in the sample database with an area under the receiver operating curve of 0.997–0.999 for non-self database comparisons. Log odds were converted to a decision via a consideration threshold θ and a leader advantage threshold δ. Defaults were chosen to penalize misidentification 20-fold versus linkage failure. By default, complete DOB mismatches were disallowed for computational efficiency. At these settings, for non-self database comparisons, the mean probability of a proband being correctly declared to be in the sample was 0.965 (range 0.931–0.994), and the misidentification rate was 0.00249 (range 0.00123–0.00429). Correct linkage was positively associated with male gender, Black or mixed ethnicity, and the presence of diagnostic codes for severe mental illnesses or other mental disorders, and negatively associated with birth year, unknown ethnicity, residential area deprivation, and presence of a pseudopostcode (e.g. indicating homelessness). Accuracy rates would be improved further if person-unique identifiers were also used, as supported by the software. Our two largest databases were linked in 44 min via an interpreted programming language. Conclusions Fully de-identified matching with high accuracy is feasible without a person-unique identifier and appropriate software is freely available.

We know that individuals' identities can become intertwined with institutions through processes of identification. But what happens when individuals need or desire to disentangle their identities from institutions after identification has taken place? In this paper, I examine previously incarcerated men's efforts to de-identify with the institutional logics associated with prison as they transition back into society. While past work has tended to characterize de-identification efforts as ultimately successful, I reveal how individuals can remain stuck in their identifications and experience identity paralysis. In doing so I add nuance to our understanding of de-identification by revealing the factors that can contribute to its breakdown and failure. I highlight specifically the importance of identity material and the content of institutional logics in influencing de-identification processes, and thus extend the existing focus on the cognitive accessibility of logics. Importantly, I reveal that challenges to de-identification result not merely from the individuals "holding on" to old targets but rather from their becoming stuck and resigning themselves to existing targets when they simultaneously feel they lack identity material to realize new identities and face negative social evaluations. Indeed my findings point to the powerful and durable ways in which institutions shape our identities even when we desire to change them. [ABSTRACT FROM AUTHOR]

Introduction Using data in research often requires that the data first be de-identified, particularly in the case of health data, which often include Personal Identifiable Information (PII) and/or Personal Health Identifying Information (PHII). There are established procedures for de-identifying structured data, but de-identifying clinical notes, electronic health records, and other records that include free text data is more complex. Several different ways to achieve this are documented in the literature. This scoping review identifies categories of de-identification methods that can be used for free text data. Methods We adopted an established scoping review methodology to examine review articles published up to May 9, 2022, in Ovid MEDLINE; Ovid Embase; Scopus; the ACM Digital Library; IEEE Explore; and Compendex. Our research question was: What methods are used to de-identify free text data? Two independent reviewers conducted title and abstract screening and full-text article screening using the online review management tool Covidence. Results The initial literature search retrieved 3,312 articles, most of which focused primarily on structured data. Eighteen publications describing methods of de-identification of free text data met the inclusion criteria for our review. The majority of the included articles focused on removing categories of personal health information identified by the Health Insurance Portability and Accountability Act (HIPAA). The de-identification methods they described combined rule-based methods or machine learning with other strategies such as deep learning. Conclusion Our review identifies and categorises de-identification methods for free text data as rule-based methods, machine learning, deep learning and a combination of these and other approaches. Most of the articles we found in our search refer to de-identification methods that target some or all categories of PHII. Our review also highlights how de-identification systems for free text data have evolved over time and points to hybrid approaches as the most promising approach for the future.

Many legal, ethical, and regulatory frameworks allow de-identified health data to be shared for research without patients’ opt-in consent. However, there may be public concerns about this practice, as people may feel they should have some say in how such data is used. This paper introduces the concept of the “inalienability of de-identified data,” to describe a key assumption underlying that public concern and preference. The assumption, derived from ethnographic research with public and professional stakeholders in AI driven medical image analysis over the past two years, refers to a sense of felt ownership over de-identified health data, even where the subject has been obscured as referent and no clear legal rights of data ownership otherwise exist. The concept is important to medical ethics because it underpins public expectations regarding the rights people should have over the sharing of medical data (including expectations for consent). We note that where those expectations go counter to current legal and bioethical frameworks for de-identified data sharing, they provide a challenge for public support of big data and artificial intelligence driven health research.

This article proposes a new method that can guarantee strong privacy while minimizing information loss in transactional data composed of a set of each attribute value in a relational database, which is not generally well-known structured data. The proposed scheme adopts the same top-down partitioning algorithm as the existing k-anonymity model, using local generalization to optimize safety and CPU execution time. At the same time, the information loss rate, which is a disadvantage of the existing local generalization, is further improved by reallocating transactions through an additional bottom-up tree search process after the partitioning process. Our scheme shows a very fast processing time compared to the HgHs algorithm using generalization and deletion techniques. In terms of information loss, our scheme shows much better performance than any schemes proposed so far, such as the existing local generalization or HgHs algorithm. In order to evaluate the efficiency of our algorithm, the experiment compared its performance with the existing local generalization and the HgHs algorithm, in terms of both execution time and information loss rate. As a result of the experiment, for example, when k is 5 in k-anonymity for the dataset BMS-WebView-2, the execution time of our scheme is up to 255 times faster than the HgHs algorithm, and with regard to the information loss rate, our method showed a maximum rate of 62.37 times lower than the local generalization algorithm. [ABSTRACT FROM AUTHOR]

The article focuses on the draft Code of Conduct for Research's interpretation of core concepts in the Protection of Personal Information Act (POPIA). It recommends improvements, including removing provisions on consent for future research, clarifying the status of special personal information, not excluding individual researchers from being responsible parties, and distinguishing de-identification in POPIA from other jurisdictions.

Abstract Human gait is a biometric that is being used in security systems because it is unique for each individual and helps recognise one from a distance without any intervention. To develop such a system, one needs a comprehensive data set specific to the application. If this data set somehow falls in the hands of rogue elements, they can easily access the secured system developed based on the data set. Thus, the protection of the gait data set becomes essential. It has been learnt that systems using deep learning are easily prone to hacking. Hence, maintaining the privacy of gait data sets in the deep learning pipeline becomes more difficult due to adversarial attacks or unauthorised access to the data set. One of the popular techniques for stopping access to the data set is using anonymisation. A reversible gait anonymisation pipeline that modifies gait geometry by morphing the images, that is, texture modifications, is proposed. Such modified data prevent hackers from making use of the data set for adversarial attacks. Nine layers were proposedto effect geometrical modifications, and a fixed gait texture template is used for morphing. Both these modify the gait data set so that any authentic person cannot be identified while maintaining the naturalness of the gait. The proposed method is evaluated using the similarity index as well as the recognition rate. The impact of various geometrical and texture modifications on silhouettes have been investigated to identify the modifications. The crowdsourcing and machine learning experiments were performed on the silhouette for this purpose. The obtained results in both types of experiments showed that texture modification has a stronger impact on the level of privacy protection than geometry shape modifications. In these experiments, the similarity index achieved is above 99%. These findings open new research directions regarding the adversarial attacks and privacy protection related to gait recognition data sets.

Background: Epidemiological research may require linkage of information from multiple organizations. This can bring two problems: (1) the information governance desirability of linkage without sharing direct identifiers, and (2) a requirement to link databases without a common person-unique identifier. Methods: We develop a Bayesian matching technique to solve both. We provide an open-source software implementation capable of de-identified probabilistic matching despite discrepancies, via fuzzy representations and complete mismatches, plus de-identified deterministic matching if required. We validate the technique by testing linkage between multiple medical records systems in a UK National Health Service Trust, examining the effects of decision thresholds on linkage accuracy. We report demographic factors associated with correct linkage. Results: The system supports dates of birth (DOBs), forenames, surnames, three-state gender, and UK postcodes. Fuzzy representations are supported for all except gender, and there is support for additional transformations, such as accent misrepresentation, variation for multi-part surnames, and name re-ordering. Calculated log odds predicted a proband's presence in the sample database with an area under the receiver operating curve of 0.997–0.999 for non-self database comparisons. Log odds were converted to a decision via a consideration threshold θ and a leader advantage threshold δ. Defaults were chosen to penalize misidentification 20-fold versus linkage failure. By default, complete DOB mismatches were disallowed for computational efficiency. At these settings, for non-self database comparisons, the mean probability of a proband being correctly declared to be in the sample was 0.965 (range 0.931–0.994), and the misidentification rate was 0.00249 (range 0.00123–0.00429). Correct linkage was positively associated with male gender, Black or mixed ethnicity, and the presence of diagnostic codes for severe mental illnesses or other mental disorders, and negatively associated with birth year, unknown ethnicity, residential area deprivation, and presence of a pseudopostcode (e.g. indicating homelessness). Accuracy rates would be improved further if person-unique identifiers were also used, as supported by the software. Our two largest databases were linked in 44 min via an interpreted programming language. Conclusions: Fully de-identified matching with high accuracy is feasible without a person-unique identifier and appropriate software is freely available. [ABSTRACT FROM AUTHOR]

This study presents a narrative review of the literature on privacy concerns of Open Government Data (OGD) programs and identifies suggested technical, procedural, and legal remedies. Peer-reviewed articles were identified and analysed from major bibliographic databases, including Web of Science, Digital ACM Library, IEEE Explore Digital Library, and Science Direct. Included articles focus on identifying individual information privacy concerns from the viewpoint of OGD stakeholders or providing solutions for mitigating concerns and risks. Papers that discussed and focused on general privacy issues or privacy concerns of open data in general or open science privacy concerns were excluded. Three streams of research were identified: 1) exploring privacy concerns and balance with OGD value propositions, 2) proposing solutions for mitigating privacy concerns, and 3) developing risk-based frameworks for the OGD program at different governmental levels. Findings suggest that contradictions with Fair Information Practices, reidentification risks, conflicts with OGD value propositions, and smart city data practices are significant privacy concerns in the literature. Proposed solutions include technical, legal, and procedural measures to mitigate privacy concerns. Building on the findings, practical implications and suggested future research directions are provided.

It is now widely known that research brain MRI, CT, and PET images may potentially be re-identified using face recognition, and this potential can be reduced by applying face-deidentification (“de-facing”) software. However, for research MRI sequences beyond T1-weighted (T1-w) and T2-FLAIR structural images, the potential for re-identification and quantitative effects of de-facing are both unknown, and the effects of de-facing T2-FLAIR are also unknown. In this work we examine these questions (where applicable) for T1-w, T2-w, T2*-w, T2-FLAIR, diffusion MRI (dMRI), functional MRI (fMRI), and arterial spin labelling (ASL) sequences. Among current-generation, vendor-product research-grade sequences, we found that 3D T1-w, T2-w, and T2-FLAIR were highly re-identifiable (96–98%). 2D T2-FLAIR and 3D multi-echo GRE (ME-GRE) were also moderately re-identifiable (44–45%), and our derived T2* from ME-GRE (comparable to a typical 2D T2*) matched at only 10%. Finally, diffusion, functional and ASL images were each minimally re-identifiable (0–8%). Applying de-facing with mri_reface version 0.3 reduced successful re-identification to ≤8%, while differential effects on popular quantitative pipelines for cortical volumes and thickness, white matter hyperintensities (WMH), and quantitative susceptibility mapping (QSM) measurements were all either comparable with or smaller than scan-rescan estimates. Consequently, high-quality de-facing software can greatly reduce the risk of re-identification for identifiable MRI sequences with only negligible effects on automated intracranial measurements. The current-generation echo-planar and spiral sequences (dMRI, fMRI, and ASL) each had minimal match rates, suggesting that they have a low risk of re-identification and can be shared without de-facing, but this conclusion should be re-evaluated if they are acquired without fat suppression, with a full-face scan coverage, or if newer developments reduce the current levels of artifacts and distortion around the face.

The proliferation of media-sharing platforms has led to issues with illegally edited content and the distribution of pornography. To protect personal information, de-identification technologies are being developed to prevent facial identification. Existing de-identification methods directly alter the pixel values in the face region, leading to reduced feature representation and identification accuracy. This study aims to develop a method that minimizes the possibility of personal identification while effectively preserving important features for image- and video-copy-detection tasks, proposing a new deep-learning-based de-identification approach that surpasses traditional pixel-based alteration methods. We introduce two de-identification models using different approaches: one emphasizing the contours of the original face through feature inversion and the other generating a blurred version of the face using D2GAN (Dual Discriminator Generative Adversarial Network). Both models were evaluated on their performance in image- and video-copy-detection tasks before and after de-identification, demonstrating effective feature preservation. This research presents new possibilities for personal-information protection and digital-content security, contributing to digital-rights management and law enforcement.

With the progress of technology in modern hospitals, an intelligent perioperative situation recognition will gain more relevance due to its potential to substantially improve surgical workflows by providing situation knowledge in real-time. Such knowledge can be extracted from image data by machine learning techniques but poses a privacy threat to the staff’s and patients’ personal data. De-identification is a possible solution for removing visual sensitive information. In this work, we developed a YOLO v3 based prototype to detect sensitive areas in the image in real-time. These are then deidentified using common image obfuscation techniques. Our approach shows that it is principle suitable for de-identifying sensitive data in OR images and contributes to a privacyrespectful way of processing in the context of situation recognition in the OR.

There is a growing trend in social science research to share qualitative data in a repository for others to access. However, some researchers are reticent to share qualitative data. One major concern is how to de-identify qualitative data while maintaining adequate contextual detail to allow secondary users to meaningfully interpret de-identified data. To help inform discussions regarding qualitative data sharing, we reviewed 100 qualitative health science studies to determine what potentially identifying variables (PIVs) are reported in the published literature. There are relatively few PIVs reported in each qualitative study; the majority of studies (n=64) reported two or fewer PIVs. The most commonly reported PIVs were profession, sex or gender, and age. Our findings can help guide de-identification efforts going forward as presumably the PIVs that are most commonly reported provide essential contextual details that will also be needed by secondary users, while PIVs that are rarely reported may not provide essential contextual information for interpretation of data. This suggests it is possible to share qualitative data that are both de-identified and useful for secondary analysis. As data are shared, we recommend researchers mask study sites, as these may uniquely increase the chance of re-identification.

Smart cities, leveraging IoT technologies, are revolutionizing the quality of life for citizens. However, the massive data generated in these cities also poses significant privacy risks, particularly in de-anonymization and re-identification. This survey focuses on the privacy concerns and commonly used techniques for data protection in smart cities, specifically addressing geolocation data and video surveillance. We categorize the attacks into linking, predictive and inference, and side-channel attacks. Furthermore, we examine the most widely employed de-identification and anonymization techniques, highlighting privacy-preserving techniques and anonymization tools; while these methods can reduce the privacy risks, they are not enough to address all the challenges. In addition, we argue that de-identification must involve properties such as unlikability, selective disclosure and self-sovereignty. This paper concludes by outlining future research challenges in achieving complete de-identification in smart cities. [ABSTRACT FROM AUTHOR]

This study presents a narrative review of the literature on privacy concerns of Open Government Data (OGD) programs and identifies suggested technical, procedural, and legal remedies. Peer-reviewed articles were identified and analysed from major bibliographic databases, including Web of Science, Digital ACM Library, IEEE Explore Digital Library, and Science Direct. Included articles focus on identifying individual information privacy concerns from the viewpoint of OGD stakeholders or providing solutions for mitigating concerns and risks. Papers that discussed and focused on general privacy issues or privacy concerns of open data in general or open science privacy concerns were excluded. Three streams of research were identified: 1) exploring privacy concerns and balance with OGD value propositions, 2) proposing solutions for mitigating privacy concerns, and 3) developing risk-based frameworks for the OGD program at different governmental levels. Findings suggest that contradictions with Fair Information Practices, reidentification risks, conflicts with OGD value propositions, and smart city data practices are significant privacy concerns in the literature. Proposed solutions include technical, legal, and procedural measures to mitigate privacy concerns. Building on the findings, practical implications and suggested future research directions are provided. [ABSTRACT FROM AUTHOR]

Abstract Background Defacing has become mandatory for anonymization of brain MRI scans; however, concerns regarding data integrity were raised. Thus, we systematically evaluated the effect of different defacing procedures on automated brain atrophy estimation. Methods In total, 268 Alzheimer’s disease patients were included from ADNI, which included unaccelerated (n = 154), within-session unaccelerated repeat (n = 67) and accelerated 3D T1 imaging (n = 114). Atrophy maps were computed using the open-source software veganbagel for every original, unmodified scan and after defacing using afni_refacer, fsl_deface, mri_deface, mri_reface, PyDeface or spm_deface, and the root-mean-square error (RMSE) between z-scores was calculated. RMSE values derived from unaccelerated and unaccelerated repeat imaging served as a benchmark. Outliers were defined as RMSE > 75th percentile and by using Grubbs’s test. Results Benchmark RMSE was 0.28 ± 0.1 (range 0.12–0.58, 75th percentile 0.33). Outliers were found for unaccelerated and accelerated T1 imaging using the 75th percentile cutoff: afni_refacer (unaccelerated: 18, accelerated: 16), fsl_deface (unaccelerated: 4, accelerated: 18), mri_deface (unaccelerated: 0, accelerated: 15), mri_reface (unaccelerated: 0, accelerated: 2) and spm_deface (unaccelerated: 0, accelerated: 7). PyDeface performed best with no outliers (unaccelerated mean RMSE 0.08 ± 0.05, accelerated mean RMSE 0.07 ± 0.05). The following outliers were found according to Grubbs’s test: afni_refacer (unaccelerated: 16, accelerated: 13), fsl_deface (unaccelerated: 10, accelerated: 21), mri_deface (unaccelerated: 7, accelerated: 20), mri_reface (unaccelerated: 7, accelerated: 6), PyDeface (unaccelerated: 5, accelerated: 8) and spm_deface (unaccelerated: 10, accelerated: 12). Conclusion Most defacing approaches have an impact on atrophy estimation, especially in accelerated 3D T1 imaging. Only PyDeface showed good results with negligible impact on atrophy estimation.

Brain imaging research studies increasingly use “de-facing” software to remove or replace facial imagery before public data sharing. Several works have studied the effects of de-facing software on brain imaging biomarkers by directly comparing automated measurements from unmodified vs de-faced images, but most research brain images are used in analyses of correlations with cognitive measurements or clinical statuses, and the effects of de-facing on these types of imaging-to-cognition correlations has not been measured. In this work, we focused on brain imaging measures of amyloid (A), tau (T), neurodegeneration (N), and vascular (V) measures used in Alzheimer’s Disease (AD) research. We created a retrospective sample of participants from three age- and sex-matched clinical groups (cognitively unimpaired, mild cognitive impairment, and AD dementia, and we performed region- and voxel-wise analyses of: hippocampal volume (N), white matter hyperintensity volume (V), amyloid PET (A), and tau PET (T) measures, each from multiple software pipelines, on their ability to separate cognitively defined groups and their degrees of correlation with age and Clinical Dementia Rating (CDR)–Sum of Boxes (CDR-SB). We performed each of these analyses twice: once with unmodified images and once with images de-faced with leading de-facing software mri_reface, and we directly compared the findings and their statistical strengths between the original vs. the de-faced images. Analyses with original and with de-faced images had very high agreement. There were no significant differences between any voxel-wise comparisons. Among region-wise comparisons, only three out of 55 correlations were significantly different between original and de-faced images, and these were not significant after correction for multiple comparisons. Overall, the statistical power of the imaging data for AD biomarkers was almost identical between unmodified and de-faced images, and their analyses results were extremely consistent.

Observational genome-wide association studies require large sample sizes. Evaluating the interplay between genomic, environmental, and lifestyle factors can require even larger sample sizes. The All of Us Research Program will recruit 1 million participants to facilitate research on genomic, environmental, and lifestyle factors. Integrating participant preferences into the research process is a new paradigm and a necessary component of the All of Us Research Program. The purpose of the study is to summarize quantitative studies of participant preferences related to participation in observational genomic research studies, starting with consent through return of results. Integrating this information into the conduct of genomic studies may benefit participants, and improve participant satisfaction, recruitment, and retention. We conducted a systematic review of the literature regarding participant views related to reconsent and broad consent, use of de-identified data, contribution of data to a biorepository, risk of identification, return of individual genetic results, and motivation for participation in genomic studies. Twenty-three articles met our inclusion and exclusion criteria. Study results found that most participants support broad consent; however, significant differences related to reconsent preferences have been shown by gender and age. Most participants support the return of individual genomic results and do not feel it is necessary to maintain a link to their de-identified data. Reasons given for joining research studies varied by population source. These findings, in addition to the knowledge that participants are more accepting of broad informed consent methods when the rationale is explained, can assist in developing guidelines for future observational genomic research.

Data sharing of large genomic databases and biorepositories provides researchers adequately powered samples to advance the goals of precision medicine. Data sharing may also introduce, however, participant privacy concerns including possible reidentification. This study compares views of research participants, genetic researchers, and institutional review board (IRB) professionals regarding concerns about the use of de-identified data. An online survey was completed by cancer patients, their relatives, and controls from the Northwest Cancer Genetics Registry (n = 450) querying views about potential harms with the use of de-identified data. This was compared to our previous online national survey of human genetic researchers (n = 351) and IRB professionals (n = 208). Researchers were less likely to feel that participants would be personally identified or harmed from a study involving de-identified data or feel that a federal agency might compel researchers to disclose information about research participants. Compared to genetic researchers, IRB professionals and participants were significantly more likely to express that personal identification or harm was likely or that researchers might be forced to disclose information by a federal agency. An understanding of the differences in views regarding possible harm from the use of de-identified data between these three important stakeholder groups is necessary to move forward with genomic research.

BackgroundData-sharing is encouraged to fulfill the ethical responsibility to transform research data into public health knowledge, but data sharing carries risks of improper disclosure and potential harm from release of individually identifiable data.MethodsThe study objective was to develop and implement a novel method for scientific collaboration and data sharing which distributes the analytic burden while protecting patient privacy. A procedure was developed where in an investigator who is external to an analytic coordinating center (ACC) can conduct original research following a protocol governed by a Publications and Presentations (P&P) Committee. The collaborating investigator submits a study proposal and, if approved, develops the analytic specifications using existing data dictionaries and templates. An original data set is prepared according to the specifications and the external investigator is provided with a complete but de-identified and shuffled data set which retains all key data fields but which obfuscates individually identifiable data and patterns; this" scrambled data set" provides a "sandbox" for the external investigator to develop and test analytic code for analyses. The analytic code is then run against the original data at the ACC to generate output which is used by the external investigator in preparing a manuscript for journal submission.ResultsThe method has been successfully used with collaborators to produce many published papers and conference reports.ConclusionBy distributing the analytic burden, this method can facilitate collaboration and expand analytic capacity, resulting in more science for less money.

The clinical application of detecting COVID-19 factors is a challenging task. The existing named entity recognition models are usually trained on a limited set of named entities. Besides clinical, the non-clinical factors, such as social determinant of health (SDoH), are also important to study the infectious disease. In this paper, we propose a generalizable machine learning approach that improves on previous efforts by recognizing a large number of clinical risk factors and SDoH. The novelty of the proposed method lies in the subtle combination of a number of deep neural networks, including the BiLSTM-CNN-CRF method and a transformer-based embedding layer. Experimental results on a cohort of COVID-19 data prepared from PubMed articles show the superiority of the proposed approach. When compared to other methods, the proposed approach achieves a performance gain of about 1–5% in terms of macro- and micro-average F1 scores. Clinical practitioners and researchers can use this approach to obtain accurate information regarding clinical risks and SDoH factors, and use this pipeline as a tool to end the pandemic or to prepare for future pandemics. [ABSTRACT FROM AUTHOR]

Natural language processing (NLP) techniques for electronic health records have shown great potential to improve the quality of medical care. The text of radiology reports frequently constitutes a large fraction of EHR data, and can provide valuable information about patients' diagnoses, medical history, and imaging findings. The lack of a major public repository for radiological reports severely limits the development, testing, and application of new NLP tools. De-identification of protected health information (PHI) presents a major challenge to building such repositories, as many automated tools for de-identification were trained or designed for clinical notes and do not perform sufficiently well to build a public database of radiology reports. We developed and evaluated six ensemble models based on three publically available de-identification tools: MIT de-id, NeuroNER, and Philter. A set of 1023 reports was set aside as the testing partition. Two individuals with medical training annotated the test set for PHI; differences were resolved by consensus. Ensemble methods included simple voting schemes (1-Vote, 2-Votes, and 3-Votes), a decision tree, a naïve Bayesian classifier, and Adaboost boosting. The 1-Vote ensemble achieved recall of 998 / 1043 (95.7%); the 3-Votes ensemble had precision of 1035 / 1043 (99.2%). F1 scores were: 93.4% for the decision tree, 71.2% for the naïve Bayesian classifier, and 87.5% for the boosting method. Basic voting algorithms and machine learning classifiers incorporating the predictions of multiple tools can outperform each tool acting alone in de-identifying radiology reports. Ensemble methods hold substantial potential to improve automated de-identification tools for radiology reports to make such reports more available for research use to improve patient care and outcomes. [ABSTRACT FROM AUTHOR]

Medical data is an important part of modern medicine. However, with the rapid increase in the amount of data, it has become hard to use this data effectively. The development of machine learning, such as feature engineering, enables researchers to capture and extract valuable information from medical data. Many deep learning methods are conducted to handle various subtasks of EHR from the view of information extraction and representation learning. This survey designs a taxonomy to summarize and introduce the existing deep learning-based methods on EHR, which could be divided into four types (Information Extraction, Representation Learning, Medical Prediction and Privacy Protection). Furthermore, we summarize the most recognized EHR datasets, MIMIC, eICU, PCORnet, Open NHS, NCBI-disease and i2b2/n2c2 NLP Research Data Sets, and introduce the labeling scheme of these datasets. Furthermore, we provide an overview of deep learning models in various EHR applications. Finally, we conclude the challenges that EHR tasks face and identify avenues of future deep EHR research. [ABSTRACT FROM AUTHOR]

Human gait is a biometric that is being used in security systems because it is unique for each individual and helps recognise one from a distance without any intervention. To develop such a system, one needs a comprehensive data set specific to the application. If this data set somehow falls in the hands of rogue elements, they can easily access the secured system developed based on the data set. Thus, the protection of the gait data set becomes essential. It has been learnt that systems using deep learning are easily prone to hacking. Hence, maintaining the privacy of gait data sets in the deep learning pipeline becomes more difficult due to adversarial attacks or unauthorised access to the data set. One of the popular techniques for stopping access to the data set is using anonymisation. A reversible gait anonymisation pipeline that modifies gait geometry by morphing the images, that is, texture modifications, is proposed. Such modified data prevent hackers from making use of the data set for adversarial attacks. Nine layers were proposedto effect geometrical modifications, and a fixed gait texture template is used for morphing. Both these modify the gait data set so that any authentic person cannot be identified while maintaining the naturalness of the gait. The proposed method is evaluated using the similarity index as well as the recognition rate. The impact of various geometrical and texture modifications on silhouettes have been investigated to identify the modifications. The crowdsourcing and machine learning experiments were performed on the silhouette for this purpose. The obtained results in both types of experiments showed that texture modification has a stronger impact on the level of privacy protection than geometry shape modifications. In these experiments, the similarity index achieved is above 99%. These findings open new research directions regarding the adversarial attacks and privacy protection related to gait recognition data sets. [ABSTRACT FROM AUTHOR]

The de-identification of clinical reports is essential to protect the confidentiality of patients. The natural-language-processing-based named entity recognition (NER) model is a widely used technique of automatic clinical de-identification. The performance of such a machine learning model relies largely on the proper selection of features. The objective of this study was to investigate the utility of various features in a conditional-random-field (CRF)-based NER model. Natural language processing (NLP) toolkits were used to annotate the protected health information (PHI) from a total of 10,239 radiology reports that were divided into seven types. Multiple features were extracted by the toolkit and the NER models were built using these features and their combinations. A total of 10 features were extracted and the performance of the models was evaluated based on their precision, recall, and F1-score. The best-performing features were n-gram, prefix-suffix, word embedding, and word shape. These features outperformed others across all types of reports. The dataset we used was large in volume and divided into multiple types of reports. Such a diverse dataset made sure that the results were not subject to a small number of structured texts from where a machine learning model can easily learn the features. The manual de-identification of large-scale clinical reports is impractical. This study helps to identify the best-performing features for building an NER model for automatic de-identification from a wide array of features mentioned in the literature. [ABSTRACT FROM AUTHOR]