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Abstract The overgeneralisation may happen because most studies on data publishing for multiple sensitive attributes (SAs) have not considered the personalised privacy requirement. Furthermore, sensitive information disclosure may also be caused by these personalised requirements. To address the matter, this article develops a personalised data publishing method for multiple SAs. According to the requirements of individuals, the new method partitions SAs values into two categories: private values and public values, and breaks the association between them for privacy guarantees. For the private values, this paper takes the process of anonymisation, while the public values are released without this process. An algorithm is designed to achieve the privacy mode, where the selectivity is determined by the sensitive value frequency and undesirable objects. The experimental results show that the proposed method can provide more information utility when compared with previous methods. The theoretic analyses and experiments also indicate that the privacy can be guaranteed even though the public values are known to an adversary. The overgeneralisation and privacy breach caused by the personalised requirement can be avoided by the new method.

The overgeneralisation may happen because most studies on data publishing for multiple sensitive attributes (SAs) have not considered the personalised privacy requirement. Furthermore, sensitive information disclosure may also be caused by these personalised requirements. To address the matter, this article develops a personalised data publishing method for multiple SAs. According to the requirements of individuals, the new method partitions SAs values into two categories: private values and public values, and breaks the association between them for privacy guarantees. For the private values, this paper takes the process of anonymisation, while the public values are released without this process. An algorithm is designed to achieve the privacy mode, where the selectivity is determined by the sensitive value frequency and undesirable objects. The experimental results show that the proposed method can provide more information utility when compared with previous methods. The theoretic analyses and experiments also indicate that the privacy can be guaranteed even though the public values are known to an adversary. The overgeneralisation and privacy breach caused by the personalised requirement can be avoided by the new method. [ABSTRACT FROM AUTHOR]

The privacy-preserving data publishing (PPDP) problem has gained substantial attention from research communities, industries, and governments due to the increasing requirements for data publishing and concerns about data privacy. However, achieving a balance between preserving privacy and maintaining data quality remains a challenging task in PPDP. This paper presents an information-driven distributed genetic algorithm (ID-DGA) that aims to achieve optimal anonymization through attribute generalization and record suppression. The proposed algorithm incorporates various components, including an information-driven crossover operator, an information-driven mutation operator, an information-driven improvement operator, and a two-dimensional selection operator. Furthermore, a distributed population model is utilized to improve population diversity while reducing the running time. Experimental results confirm the superiority of ID-DGA in terms of solution accuracy, convergence speed, and the effectiveness of all the proposed components. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Digital data play an increasingly important role in how we understand the present and the past. The challenges inherent in understanding and using digital data are as intellectually demanding as any other archaeological research endeavor. For these reasons, data management cannot be regarded as a simple compliance or technical issue. For data to be meaningfully preserved and used in intellectually rigorous ways, they need to be integrated fully into all aspects of archaeological practice, including ethics, teaching, and publishing. In this review, we highlight some of the significant and multifaceted challenges involved in managing data, including documentation, training, methodology, data modeling, trust, and ethical concerns. We then focus on the importance of building data literacy broadly among archaeologists so that we can manage and communicate the data our discipline creates. This involves more than learning to use a new tool or finding a data manager for one's excavation or survey. Long-term, responsible stewardship of data requires understanding the workflows and human roles in data management. Putting effort now into thoughtful data management and broad data-literacy training means we will be able to make the most of the “bigger” data that archaeologists now produce. An important aspect of this reorientation will be to look beyond the boundaries of our own research projects and information systems. Future research, teaching, and public engagement needs will also compel us to explore how our data articulates with wider contexts—within and beyond our discipline.

Artificial intelligence (AI) is a transformative technology with a substantial number of practical applications in commercial sectors such as healthcare, finance, aviation, and smart cities. AI also has strong synergy with the information privacy (IP) domain from two distinct aspects: as a protection tool (i.e., safeguarding privacy), and as a threat tool (i.e., compromising privacy). In the former case, AI techniques are amalgamated with the traditional anonymization techniques to improve various key components of the anonymity process, and therefore, privacy is safeguarded effectively. In the latter case, some adversarial knowledge is aggregated with the help of AI techniques and subsequently used to compromise the privacy of individuals. To the best of our knowledge, threats posed by AI-generated knowledge such as synthetic data (SD) to information privacy are often underestimated, and most of the existing anonymization methods do not consider/model this SD-based knowledge that can be available to the adversary, leading to privacy breaches in some cases. In this paper, we highlight the role of AI as a threat tool (i.e., AI used to compromise an individual’s privacy), with a special focus on SD that can serve as background knowledge leading to various kinds of privacy breaches. For instance, SD can encompass pertinent information (e.g., total # of attributes in data, distributions of sensitive information, category values of each attribute, minor and major values of some attributes, etc.) about real data that can offer a helpful hint to the adversary regarding the composition of anonymized data, that can subsequently lead to uncovering the identity or private information. We perform reasonable experiments on a real-life benchmark dataset to prove the pitfalls of AI in the data publishing scenario (when a database is either fully or partially released to public domains for conducting analytics).

Abstract Most of the data publishing methods have not considered sensitivity protection, and hence the adversary can disclose privacy by sensitivity attack. Faced with this problem, this paper presents a medical data publishing method based on sensitivity determination. To protect the sensitivity, the sensitivity of disease information is determined by semantics. To seek the trade‐off between information utility and privacy security, the new method focusses on the protection of sensitive values with high sensitivity and assigns the highly sensitive disease information to groups as evenly as possible. The experiments are conducted on two real‐world datasets, of which the records include various attributes of patients. To measure sensitivity protection, the authors define a metric, which can evaluate the degree of sensitivity disclosure. Besides, additional information loss and discernability metrics are used to measure the availability of released tables. The experimental results indicate that the new method can provide better privacy than the traditional one while the information utility is guaranteed. Besides value protection, the proposed method can provide sensitivity protection and available releasing for medical data.

Data publishing techniques have led to breakthroughs in several areas. These tools provide a promising direction. However, when they are applied to private or sensitive data such as patient medical records, the published data may divulge critical patient information. In order to address this issue, we propose a differential private data publishing method (SSKM_DP) based on the SFLA-Kohonen network, which perturbs sensitive attributes based on the maximum information coefficient to achieve a trade-off between security and usability. Additionally, we introduced a single-population frog jump algorithm (SFLA) to optimize the network. Extensive experiments on benchmark datasets have demonstrated that SSKM_DP outperforms state-of-the-art methods for differentially private data publishing techniques significantly. [ABSTRACT FROM AUTHOR]

For clustering applications in the field of trajectory privacy protection, the k-means algorithm is sensitive to initial values and the number of clusters may be somewhat arbitrary. To address these issues and further improve the usability of clustering results, a trajectory privacy protection scheme combining k-shape and differential privacy (KSDP) is proposed. Firstly, the trajectory data is partitioned and preprocessed based on the temporal and spatial attributes of the trajectory to improve the quality of clustering generalization. Secondly, a utility function is used to evaluate the preprocessed trajectory data, and the clustering generalization is performed after filtering the data. Finally, Laplace noise is added to the generalized data to satisfy the differential privacy protection model, so as to further protect the trajectory privacy. The experimental simulation results show that compared with the traditional differential privacy k-means clustering scheme, the KSDP scheme effectively improves the availability of clustering results and achieves better trajectory data publishing and privacy protection. [ABSTRACT FROM AUTHOR]

With the rapid development of location-based service technology, the leakage of trajectory privacy has become more and more serious. In order to solve the problems of insufficient privacy protection and low availability of published data in the existing trajectory privacy protection models, we propose a spatiotemporal generalized trajectory data publishing algorithm SGTP based on differential privacy. Firstly, a spatiotemporal generalization method of trajectories based on clustering is designed. The temporal location set is divided by a density peak trajectory clustering algorithm (DPTC), and the location is probabilistically generalized combined with an exponential mechanism to hide the real location information of users. Secondly, random noise is added to the generalized trajectory statistics by the Laplace mechanism, and the noise is post-processed by consistency constraints to improve the utility of the published data without affecting the privacy of the trajectories. Finally, we theoretically demonstrate that SGTP strictly satisfies differential privacy. Experimental results based on publicly available data show that SGTP can effectively protect user privacy and guarantee data utility and at the same time has a higher execution efficiency. [ABSTRACT FROM AUTHOR]

Privacy-preserving algorithm based on k-anonymity plays an outstanding role in real-world data mining applications, such as medical records, bioinformatics, market, and social network. How to maximize the availability of published data without sacrificing users' privacy is the emphasis of privacy-preserving research. In this paper, we propose a mixed-feature weighted clustering algorithm for k-anonymity (MWCK) to study the contradiction of efficiency and information loss for utility-type anonymization. First, we propose the concept of natural equivalence group, then tuples with same attributes in dataset can be pre-extracted to reduce time complexity and information loss. Second, a sorting algorithm based on the shortest distance is proposed, which selects the optimal initial cluster center at a lower computational cost to reduce the number of iterations. Finally, MWCK not only considers intra-cluster isomorphism to reduce generalization information loss and inter-cluster heterogeneity to avoid local optimal solutions, but also applies to both numerical and categorical datasets. Extensive experiments show that our algorithm can effectively protect data privacy and has better comprehensive performance in terms of information loss and computational complexity than state-of-art methods. [ABSTRACT FROM AUTHOR]

In recent years, location-based social media has become popular, and a large number of spatiotemporal trajectory data have been generated. Although these data have significant mining value, they also pose a great threat to the privacy of users. At present, many studies have realized the privacy-preserving mechanism of location data in social media in terms of data utility and privacy preservation, but rarely have any of them considered the correlation between timestamps and geographical location. To solve this problem, in this paper, we first propose a k-anonymity-based mechanism to hide the user's specific time segment during a single day, and then propose an optimized truncated Laplacian mechanism to add noise to each data grid (the frequency of time data) of the anonymized time distribution. The time data after secondary processing are fuzzy and uncertain, which not only protects the privacy of the user's geographical location from the time dimension but also retains a certain value of data mining. Experiments on real datasets show that the TDP privacy-preserving model has good utility. [ABSTRACT FROM AUTHOR]

In order to reduce the loss of information in data publishing, a k-anonymous data publishing algorithm based on hybrid clustering is proposed to solve the problem of low data availability in existing data anonymity schemes based on clustering. Compared with the traditional single clustering method, the proposed algorithm combines partition clustering and distance clustering, selects the initial clustering center point according to the density characteristics of the data set, and uses partition clustering to achieve the optimal clustering iteratively. In addition, the proposed method eliminates part of the outlier noise in the data set to reduce its impact on the clustering results. For hybrid data records, the distance measurement method combining k-means and k-modes is adopted, and the bucket generalization algorithm is introduced to reduce the information loss caused by generalization operation. Experimental results show that compared with the existing methods, the k-anonymity data publishing algorithm based on hybrid clustering can effectively reduce the information loss of data anonymity and improve the quality of data publishing. [ABSTRACT FROM AUTHOR]

Summary: Anonymization is a practical solution for preserving user's identity privacy before data publishing. There are various anonymity techniques can be applied to maintain data utility of micro‐data and social networks, however these methods lead to a high runtime or low anonymous graph utility. In this article, an efficient, utility‐preserving approach has been proposed to reduce anonymization runtime as well as the amount of information loss incurred by graph anonymization. We craft our anonymization algorithm by combining greedy partition‐based aggregating with multi‐dimensional sorting as main heuristic tools. The proposed algorithm generates a partial order of the vertices so that the vertex at top rank and another vertex at bottom rank can never be aggregated in the same group, the runtime is reduced. Greedy partition‐based aggregating is employed to create k$$ k $$‐anonymous clusters which minimizing information loss. Experimental results on real‐world datasets show the proposed method has good performance and is superior to the existing methods. [ABSTRACT FROM AUTHOR]

Advancements in Industry 4.0 brought tremendous improvements in the healthcare sector, such as better quality of treatment, enhanced communication, remote monitoring, and reduced cost. Sharing healthcare data with healthcare providers is crucial for harnessing the benefits of such improvements. In general, healthcare data holds sensitive information about individuals. Hence, sharing such data is challenging because of various security and privacy issues. According to privacy regulations and ethical requirements, it is essential to preserve the privacy of patients before sharing data for medical research. State-of-the-art literature on privacy preserving studies either uses cryptographic approaches to protect the privacy or uses anonymizing techniques regardless of the type of attributes, this results in poor protection and data utility. In this paper, we propose an attribute-focused privacy preserving data publishing scheme. The proposed scheme is two-fold, comprising a fixed-interval approach to protect numerical attributes and an improved $l$ -diverse slicing approach to protect the categorical and sensitive attributes. In the fixed-interval approach, the original values of the healthcare data are replaced with an equivalent computed value. The improved $l$ -diverse slicing approach partitions the data both horizontally and vertically to avoid privacy leaks. Extensive experiments with real-world datasets are conducted to evaluate the performance of the proposed scheme. The classification models built on anonymized dataset yields approximately 13% better accuracy than benchmarked algorithms. Experimental analyses show that the average information loss which is measured by normalized certainty penalty (NCP) is reduced by 12% compared to similar approaches. The attribute focused scheme not only provides data utility but also prevents the data from membership disclosures, attribute disclosures, and identity disclosures.

Privacy-preserving data publishing (PPDP) is an essential prerequisite for data-driven AI technologies, (such as data mining, machine learning, deep learning, etc.) to extract knowledge from data safely and legally. It has, as it should be, been studied and explored as a hot topic in the last decade. However, existing privacy protection mechanisms cannot take into account the following three aspects: preventing background attack, maximizing data availability, and resisting sensitive information mining. In this work, we propose a novel privacy-preserving data publishing framework, which protects privacy by releasing simulated data instead of real data. It is explored for generating data similar to the distribution of the real data by using Bayesian network. It consists of two ingredients. First, we transform the problem of data publication into the generation process of a Bayesian network, and correspondingly, the problem of privacy leakage is transformed into one kind of Bayesian inference attack. Second, we propose a re-anonymity framework, named (d, L)-injection, which flexibly resolves the impact of increased privacy protection strength on data availability. In addition, we transplant three classical privacy-preserving strategies to the generated Bayesian network, and demonstrates the effectiveness of the method through three public data sets from multiple application domains.

• Considering the diversity of original trajectories to improve the utility of dataset. • Adding different sizes of noise to the count value can improve the utility of the data. • Considering spatiotemporal characteristics of trajectories can improve privacy protection degree. Promoted by the Internet of Thing era, the widespread use of mobile sensing devices equipped with positioning functions has led to the generation of substantial trajectory data. Mining and analyzing trajectory data has high research value, but poses a risk of user privacy leakage, resulting in fewer publicly available trajectory datasets for research and analysis. Therefore, a trajectory data publishing method that ensures high data utility while protecting user privacy has become a hot topic. In this paper, we propose a spatiotemporal trajectory data protection and publishing method based on kernel density estimation (STP-KDE), which protects the trajectory data and trajectory count values while improving data utility. In the protection process of trajectory data, we designed a kernel density clustering framework that is combined with the differential privacy exponential mechanism. In the protection process of trajectory counts value, the adaptive Laplace noise perturbation mechanism is proposed to differentially protect these counts. Experimental results show that STP-KDE can provide more useful data and stronger privacy protection than existing studies. [ABSTRACT FROM AUTHOR]

Most of the data publishing methods have not considered sensitivity protection, and hence the adversary can disclose privacy by sensitivity attack. Faced with this problem, this paper presents a medical data publishing method based on sensitivity determination. To protect the sensitivity, the sensitivity of disease information is determined by semantics. To seek the trade‐off between information utility and privacy security, the new method focusses on the protection of sensitive values with high sensitivity and assigns the highly sensitive disease information to groups as evenly as possible. The experiments are conducted on two real‐world datasets, of which the records include various attributes of patients. To measure sensitivity protection, the authors define a metric, which can evaluate the degree of sensitivity disclosure. Besides, additional information loss and discernability metrics are used to measure the availability of released tables. The experimental results indicate that the new method can provide better privacy than the traditional one while the information utility is guaranteed. Besides value protection, the proposed method can provide sensitivity protection and available releasing for medical data. [ABSTRACT FROM AUTHOR]

Publishing big data and making it accessible to researchers is important for knowledge building as it helps in applying highly efficient methods to plan, conduct, and assess scientific research. However, publishing and processing big data poses a privacy concern related to protecting individuals' sensitive information while maintaining the usability of the published data. Several anonymization methods, such as slicing and merging, have been designed as solutions to the privacy concerns for publishing big data. However, the major drawback of merging and slicing is the random permutation procedure, which does not always guarantee complete protection against attribute or membership disclosure. Moreover, merging procedures may generatemany fake tuples, leading to a loss of data utility and subsequent erroneous knowledge extraction. This study therefore proposes a slicingbased enhanced method for privacy-preserving big data publishing while maintaining the data utility. In particular, the proposed method distributes the data into horizontal and vertical partitions. The lower and upper protection levels are then used to identify the unique and identical attributes' values. The unique and identical attributes are swapped to ensure the published big data is protected from disclosure risks. The outcome of the experiments demonstrates that the proposed method could maintain data utility and provide stronger privacy preservation. [ABSTRACT FROM AUTHOR]

Quasi-identifier is a set of attributes used to identify the specific entity in structured data, which can provide an inference path for query attacks. Improper selection of quasi-identifiers leads to the failure of current privacy-preserving data publishing. In this paper, we propose a method of solving quasi-identifiers based on functional dependency to ensure the accuracy and completeness of the selected quasi-identifiers for relational data publishing. First, we partition the identifying attributes and sensitive attributes in the relational scheme of relational data published according to the semantic relationship and publishing requirements. Second, we mine the dependencies on identifying attributes with other attributes in the relational schema according to semantics and instance data in relational data, subsequently we can obtain complete quasi-identifiers. Finally, we implement the algorithm for solving quasi-identifiers in Python language, and solve quasi-identifiers on three actual data sets of different sizes, and afterward use the model of 3-anonymity, 2-diversity, and 1-differential privacy for privacy protection experiments. The results demonstrate that the average group records of equivalent class divided on the solved quasi-identifier is 8% smaller than other five methods, and the probability of privacy disclosure is reduced by about 3%. So, the accuracy and completeness of our method are better than other five methods.

Data publishing techniques have led to breakthroughs in several areas. These tools provide a promising direction. However, when they are applied to private or sensitive data such as patient medical records, the published data may divulge critical patient information. In order to address this issue, we propose a differential private data publishing method (SSKM_DP) based on the SFLA-Kohonen network, which perturbs sensitive attributes based on the maximum information coefficient to achieve a trade-off between security and usability. Additionally, we introduced a single-population frog jump algorithm (SFLA) to optimize the network. Extensive experiments on benchmark datasets have demonstrated that SSKM_DP outperforms state-of-the-art methods for differentially private data publishing techniques significantly.

The tremendous growth of Internet of Medical Things has led to a surge in medical user data, and medical data publishing can provide users with numerous services. However, neglectfully publishing the data may lead to severe leakage of user's privacy. In this article, we investigate the problem of data publishing in Internet of Medical Things with privacy preservation. We present a novel system model for Internet of Medical Things user data publishing which adopts the proposed multiple partition differential privacy k -medoids clustering algorithm for data clustering analysis to ensure the security of user data. Particularly, we propose a multiple partition differential privacy k -medoids clustering algorithm based on differential privacy in data publishing. Based on the traditional k -medoids clustering, multiple partition differential privacy k -medoids clustering algorithm optimizes the randomness of selecting initial center points and adds Laplace noise to the clustering process to improve data availability while protecting user's privacy information. Comprehensive analysis and simulations demonstrate that our method can not only meet the requirements of differential privacy but also retain the better availability of data clustering. [ABSTRACT FROM AUTHOR]

Open Science movement has enabled extensive knowledge sharing by making research publications, software, data and samples available to the society and researchers. The demand for data sharing is increasing day by day due to the tremendous knowledge hidden in the digital data that is generated by humans and machines. However, data cannot be published as such due to the information leaks that can occur by linking the published data with other publically available datasets or with the help of some background knowledge. Various anonymization techniques have been proposed by researchers for privacy preserving sensitive data publishing. This paper proposes a (k,n,m) anonymity approach for sensitive data publishing by making use of the traditional k-anonymity technique. The selection of quasi identifiers is automated in this approach using graph theoretic algorithms and is further enhanced by choosing similar quasi identifiers based on the derived and composite attributes. The usual method of choosing a single value of ‘k’ is modified in this technique by selecting different values of ‘k’ for the same dataset based on the risk of exposure and sensitivity rank of the sensitive attributes. The proposed anonymity approach can be used for sensitive big data publishing after applying few extension mechanisms. Experimental results show that the proposed technique is practical and can be implemented efficiently on a plethora of datasets.

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Copyright of Journal of Polytechnic is the property of Journal of Polytechnic and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)