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In 2019, the UK's Immigration and Asylum Chamber of the Upper Tribunal dismissed an asylum appeal basing the decision on the output of a biometric system, alongside other discrepancies. The fingerprints of the asylum seeker were found in a biometric database which contradicted the appellant's account. The Tribunal found this evidence unequivocal and denied the asylum claim. Nowadays, the proliferation of biometric systems is shaping public debates around its political, social and ethical implications. Yet whilst concerns towards the racialised use of this technology for migration control have been on the rise, investment in the biometrics industry and innovation is increasing considerably. Moreover, fairness has also been recently adopted by biometrics to mitigate bias and discrimination on biometrics. However, algorithmic fairness cannot distribute justice in scenarios which are broken or intended purpose is to discriminate, such as biometrics deployed at the border. In this paper, we offer a critical reading of recent debates about biometric fairness and show its limitations drawing on research in fairness in machine learning and critical border studies. Building on previous fairness demonstrations, we prove that biometric fairness criteria are mathematically mutually exclusive. Then, the paper moves on illustrating empirically that a fair biometric system is not possible by reproducing experiments from previous works. Finally, we discuss the politics of fairness in biometrics by situating the debate at the border. We claim that bias and error rates have different impact on citizens and asylum seekers. Fairness has overshadowed the elephant in the room of biometrics, focusing on the demographic biases and ethical discourses of algorithms rather than examine how these systems reproduce historical and political injustices., Comment: 14 pages, 3 figures

Due to the characteristics of limited resources and dynamic topology, wireless sensor networks (WSNs) are facing two major problems: security and energy consumption. Nowadays, the trust based solutions are feasible to cope with different bad behaviors of nodes, but there still exist a variety of attacks, high energy consumption and communication congestion between nodes. Therefore, this paper proposes a new trust based secure and energy efficient routing protocol (TBSEER) to solve these problems. TBSEER calculates the comprehensive trust value through adaptive direct trust value, indirect trust value and energy trust value, which can be resistant to black hole, selective forwarding, sinkhole and hello flood attacks. Moreover, the adaptive penalty mechanism and volatilization factor are used to fast identify the malicious nodes. In addition, the nodes only need to calculate the direct trust value, and the indirect trust value is obtained by the Sink, so as to further reduce the energy consumption caused by iterative calculations. Finally, the cluster heads find the safest multi-hop routes based on the comprehensive trust value, which can actively avoid wormhole attack. The simulation results show that the proposed TBSEER reduces network energy consumption, speeds up the identification of malicious nodes, as well as resists all common attacks.

With the rapid growth of cloud email services, email encryption is beginning to be used more and more to alleviate concerns about cloud privacy and security. However, this increase in usage invites the problem of how to search and filter encrypted emails effectively. Searchable public key encryption is a popular technology to solve encrypted email searching, but encrypted email filtering is still an open problem. We propose an encrypted cloud email searching and filtering scheme based on hidden policy ciphertext-policy attribute-based encryption with keyword search as a new solution. It enables the recipient to search the encrypted cloud email keywords and allows the email filtering server to filter the encrypted email content when receiving the email, as the traditional email keyword filtering service. Our hidden policy scheme is constructed by composite order bilinear groups and proven secure by dual system encryption methodology. Our scheme can be applied to other scenarios such as file searching and filtering and has certain practical value.

To protect the medical images integrity, digital watermark is embedded into the medical images. A non-blind medical image watermarking scheme based on hybrid transform is propounded. In this paper, fingerprint of the patient is used as watermark for better authentication, identifying the original medical image and privacy of the patients. In this scheme, lifting wavelet transform (LWT) and discrete wavelet transform (DWT) are utilized for amplifying the watermarking algorithm. The scaling and embedding factors are calculated adaptively with the help of Local Binary Pattern values of the host medical image to achieve better imperceptibility and robustness for medical images and fingerprint watermark, respectively. Two-level decomposition is done where for the first level LWT is utilized and for the second level decomposition DWT is utilized. At the extraction side, non-blind recovery of fingerprint watermark is performed which is similar to the embedding process. The propounded design is implemented on various medical images like Chest X-ray, CT scan and so on. The propounded design provides better imperceptibility and robustness with the combination of LWT-DWT. The result analysis proves that the proposed fingerprint watermarking scheme has attained best results in terms of robustness and authentication with different medical image attacks. Peak Signal to Noise Ratio and Normalized Correlation Coefficient metrics are used for evaluating the proposed scheme. Furthermore, superior results are obtained when compared to related medical image watermarking schemes.

Biometric authentication is, over time, becoming an indispensable complementary component to traditional authentication methods that use passwords and tokens. As a result, the research interest in the protection techniques for the biometric template has also grown considerably. In this paper, we present a light-weight AI-based biometric authentication that operates based on the binary representation of a biometric instance. In details, a binary classifier will be trained using the binary strings that represent the intraclass and interclass biometric subjects. The Support Vector Machine and Multi-layer Perceptron Neural Network are chosen as the classifier to evaluate the fingerprint-based and iris-based authentication capability. Afterward, the authenticated biometric string is fed to a hash function to produce a hash value, which is to be used in a Zero-Knowledge-Proof Protocol for the purpose of privacy preservation. In order to improve the recognition of the classifier, we devise a simple yet efficient strategy to enhance the discriminativeness of the binary strings and name it the Composite Features Retrieval. We evaluated the proposed method with the four publicly available fingerprint datasets FVC2002-DB1, FVC2002-DB2, FVC2002-DB3, and FVC2004-DB2 and the iris dataset UBIRISv1. The promising performance shows this method's capability.

Refereed/Peer-reviewed Video watermarking techniques can be used to prevent unauthorized users from illegally distributing videos across (social) media networks. However, current watermarking solutions are unable to embed a perceptually invisible watermark which is robust to the distortions introduced by camcording. These watermark-disrupting distortions include lossy compression, the addition of noise, frame-rate conversion and geometric distortions. In this paper, we present a novel video watermarking technique that is blind and robust to camcording attacks. The proposed approach uses the integration of the dual-tree complex wavelet transform (DTCWT) and singular value decomposition (SVD) to achieve robustness against geometric attacks. The experimental results validate our technique’s superior imperceptibility and robustness to several attacks when compared to existing peer mechanisms. In conclusion, the proposed technique can be used to protect against illegal distribution of video content.

The privacy paradox suggests that privacy concerns do not relate to privacy-related behavior. Although it has inspired numerous studies, findings remain inconclusive. Some of the inconsistencies in published findings may be explained by a strong heterogeneity in the conceptual and analytical choices that researchers implement when investigating the privacy paradox. Based on representative survey data of the 27 EU member states (2011: n = 8,962; 2015: n = 10,526; 2019: n = 11,428), I investigated the effect of conceptual and analytical decisions on ‘finding’ the privacy paradox. Specification curve analyses revealed that the magnitude and statistical significance of the relationship between privacy concerns and information disclosure is contingent on the operationalization of the independent variable, the inclusion of covariates, and the age of the studied population. The relationship between online privacy concerns and using social media privacy settings, in contrast, was less influenced by analytical decisions. Yet, the relationship was stronger in younger people and increased over time. The findings call for more transparency in analyzing research data. Evaluating the implications of analytical choices will help to establish best practices and advance cumulative knowledge creation in privacy research.

Trust in financial institutions is widely considered important. However, a clear overview of studies on the drivers of trust is missing. We intend to fill this gap in the literature. After discussing why trust in financial institutions is important, we turn to its measurement, where we distinguish between trust in one's own institution and trust in institutions in general (narrow-scope and broad-scope trust), and discuss how these measures differ from generalized trust (i.e. trust in other people with whom there is no direct relationship). Finally, we survey the determinants of trust in financial institutions and discuss a wide range of drivers. First, trust in financial institutions depends on the economic situation: it behaves procyclically and is negatively affected by financial crises. Second, the behavior of financial institutions matters: prudent conduct, the provision of good services and financial health have a positive effect on trust. Third, although consumer characteristics also relate to trust, many of these relationships are context-dependent. Fourth, there is a positive association between narrow-scope trust on the one hand and broad-scope trust and generalized trust on the other. Last, policy measures and supervisory actions can help prevent loss of trust.

We proposed architecture for sharing electronic health records. This work is based on an encryption mechanism to encrypt the health data, access control to ensure the privacy and confidentiality of health records. The proposed scheme has used a storage mechanism combining cloud and blockchain. The cloud server stores the encrypted health records, while the blockchain retains traceable log information and encryption keys. The proposal is an adequate solution to share the electronic health record securely. Our solution allows integrity, privacy, and confidentiality to ensure efficient protection of sharing electronic health records.

It is widely known that the analysis of fingerprints is a method which can lead to the correct identification of a person. It is lesser known that the accuracy of this method is endangered, when it comes to using digital technology, in order to store and compare information regarding fingerprints. The use of digital technology in analysing fingerprints is very important, because it enables various activities which are vital for a criminal investigation, for example the comparison between a certain fingerprint, found at crime scene, with the fingerprints which are stored in databases. However, storing fingerprints in a digital form is not a simple task, as the information must be protected from potential threats. A simple way to protect the information regarding fingerprints is to encrypt the information, but this means that, when information is transferred, the receptor must have the appropriate key, in order to decrypt the information. This leads to the necessity of a standard algorithm, which must be used by both the sender and the receiver. This paper analyses the challenges of creating such a standard algorithm and assesses the implications of standardization in fingerprinting.

The current study investigated the role of sequential auditory memory and gender in auditory comprehension among a sample of (80) primary school students, who were chosen by random method. To achieve the objectives of the study, the sequential auditory memory test was used to measure the auditory memory, and another test was prepared by the researcher to measure the auditory comprehension. The findings revealed apparent differences between the average performance of students on the sequential auditory memory test and the auditory comprehension test due to the gender variable and in favor of females. The results also indicated the variation in the auditory comprehension of sequential audio memory and the superiority of females over males in auditory comprehension. The study did not reveal an effect of the interaction between the variables of sequential auditory memory and gender in auditory comprehension.

The existing privacy protection schemes for Location‐Based Service (LBS) only protect users' location privacy or query privacy, which can not adopt both of the privacy protections simultaneously in the LBS system. Moreover, these schemes cannot take into account the spatial‐temporal correlation and background knowledge. In response to the above mentioned questions, the LBS Privacy Protection Scheme Based on Differential Privacy (DPLQ) is proposed. The method contains two kinds of privacy protection algorithms: users' location privacy protection algorithm and users' query privacy protection algorithm. The users' location privacy protection algorithm divides the map using the Voronoi diagram, choosing l fake location points based on the improved k‐means algorithm and l‐diversity idea, and protects users' location privacy with the Laplace mechanism. Based on the k‐anonymous algorithm, the users' query privacy protection algorithm builds a query k‐anonymous set according to the neighbour users' query requests at the same time t in the cluster and the historical query probability of the region’s POI and protects users' query privacy with the exponential mechanism. Through setting the privacy protection intensity of the algorithm by the users, the generated location dataset and query k‐anonymous set can resist a variety of attacks from malicious attackers. Theoretical analysis and experimental results show that the scheme can effectively protect the location privacy and query privacy of users.

We introduce the 2-colour parity. It is a theory of parity for a large class of virtual links, defined using the interaction between orientations of the link components and a certain type of colouring. The 2-colour parity is an extension of the Gaussian parity, to which it reduces on virtual knots. We show that the 2-colour parity descends to a parity on free links. We compare the 2-colour parity to other parity theories of virtual links, focusing on a theory due to Im and Park. The 2-colour parity yields a strictly stronger invariant than the Im-Park parity. We introduce an invariant, the 2-colour writhe, that takes the form of a string of integers. The 2-colour writhe is a concordance invariant, and so obstructs sliceness. It is also an obstruction to (±)-amphichirality and chequerboard colourability within a concordance class.

Biometrics has been developing for decades in diverse industries, such as consumer electronics, internet of things, financial industry, etc. The purpose of this research is to build a decision-making model to evaluate and improve the performances of biometrics for administrators to design and make suitable biometric systems. This paper adopts a hybrid multiple criteria decision making (MCDM) model, comprising decision-making trial and evaluation laboratory (DEMATEL), and DEMATEL-based analytic network process (called DANP) to probe into the interrelationship and influential weights among criteria of biometrics. According to DEMATEL technique, the empirical results indicate that criteria of biometrics have self-effect relationships. The dimension of biometrics that administrators of biometrics should enhance first when improving the performances is usability. The criterion of universality with the highest influencing value to systematically affect all other evaluation factors is what administrators of biometrics should comprehensively consider. In the top three criteria for evaluation by DANP, biometric systems with the most influential weight is the criterion that can be improved to have higher recognition rates for increasing the performances of biometrics, followed by biometric conditions and permanence.

In a digital society, the rapid development of computer science and the Internet has greatly facilitated image applications. However, one of the public network also brings risks to both image tampering and privacy exposure. Image authentication is the most important approaches to verify image integrity and authenticity. However, it has been challenging for image authentication to address both issues of tampering detection and privacy protection. One aspect, image authentication requires image contents not be changed to detect tampering. The other, privacy protection needs to remove sensitive information from images, and as a result, the contents should be changed. In this paper, we propose a practical image authentication scheme constructed from chameleon hashes combined with ordinary digital signatures to make tradeoff between tampering detection and privacy protection. Our scheme allows legitimate users to modify contents of authenticated images with a privacy-aware purpose (for example, cover some sensitive areas with mosaics) according to specific rules and verify the authenticity without interaction with the original authenticator. The security of our scheme is guaranteed by the security of the underlying cryptographic primitives. Experiment results show that our scheme is efficient and practical. We believe that our work will facilitate image applications where both authentication and privacy protection are desirable.

Background With a rapidly ageing society, healthy ageing has become a key challenge. Engagement in physical activity, and particularly walking, is a key strategy that contributes to healthy ageing amongst older adults. The purpose of the present study was to evaluate the efficacy of a group walking program for older adults that incorporates the 5R Shared Leadership Program (5RS). By implementing a structure of shared leadership and strengthening peer leaders’ identity leadership, 5RS aims to cultivate a shared social identity amongst participants, which has in other contexts been associated with greater performance and well-being. Methods A cluster randomised controlled trial was conducted to test the efficacy of the 5RS group walking program on group identification, group cohesion, walking activity, and well-being, compared to a regular group walking program for older adults. Nineteen older adult walking groups (i.e., the clusters; N = 503; Mage = 69.23 years, SD = 6.68) all participated in a 12-week structured group walking program. Nine walking groups (n = 304) were randomly assigned to the intervention in which participants received the 5RS program in addition to regular group walking. Results 5RS was successful in strengthening the identity leadership qualities of the appointed peer leaders. Multilevel regressions showed that 5RS succeeded in increasing group cohesion and walking activity to a greater extent than a regular group walking program, while participants’ group identification and well-being increased to a similar extent in both conditions. Furthermore, structural equation modelling revealed that group identification mediated the impact of peer leaders’ identity leadership on group cohesion and well-being (but not walking activity). Conclusion By harnessing the capacity of the group and its peer leaders, the 5RS program offers a promising intervention to engage older adults in physical activity. Trial registration The study was retrospectively registered as clinical trial on 9 September 2021 (NCT05038423).

With the rapid development of internet technology, digital images are easily accessed and tampered by unauthorized users. Therefore, copyright of color images has become an important challenge in nowadays. Robust color image watermarking methods have emerged to provide copyright. However, robust watermarking methods must satisfy the requirements such as robustness, imperceptibility, capacity, and security. Therefore, researchers are focused on methods using grayscale watermark instead of the binary watermark to increase capacity. Grayscale watermark based robust color image watermarking methods are mostly semi-blind and non-blind. However, these methods have limited utilization areas since the original image, original watermark, or side information is required. To address these drawbacks, we propose a blind robust color image watermarking method using grayscale watermark. In the proposed watermark embedding procedure, one-level discrete wavelet transform is performed on the Cb color component. Then, scrambled watermark pixels are hidden into coefficients of 4x4 discrete cosine transform (DCT) matrices of LL subband by using dividing-based new watermark embedding formula. In the watermark extracting procedure, watermark pixels are extracted from coefficients of DCT matrices by using dividing-based extracting formula. The performance is demonstrated against various attacks and experimental results indicate that the presented method ensures robustness while preserving imperceptibility.

Funder: Cambridge Trust, Funder: National Research Foundation Singapore, Funder: Alexander von Humboldt-Stiftung, Funder: China Scholarship Council, In this paper, the ability of three selected machine learning neural and baseline models in predicting the power conversion efficiency (PCE) of organic photovoltaics (OPVs) using molecular structure information as an input is assessed. The bidirectional long short-term memory (gFSI/BiLSTM), attentive fingerprints (attentive FP), and simple graph neural networks (simple GNN) as well as baseline support vector regression (SVR), random forests (RF), and high-dimensional model representation (HDMR) methods are trained to both the large and computational Harvard clean energy project database (CEPDB) and the much smaller experimental Harvard organic photovoltaic 15 dataset (HOPV15). It was found that the neural-based models generally performed better on the computational dataset with the attentive FP model reaching a state-of-the-art performance with the test set mean squared error of 0.071. The experimental dataset proved much harder to fit, with all of the models exhibiting a rather poor performance. Contrary to the computational dataset, the baseline models were found to perform better than the neural models. To improve the ability of machine learning models to predict PCEs for OPVs, either better computational results that correlate well with experiments or more experimental data at well-controlled conditions are likely required.

In authentication protocols, anonymity is for privacy, while deniability is for anti-forensics after completion of the protocols. We propose a syntax and security definitions of an anonymous deniable predicate authentication scheme with revocability (rADPA). This new cryptographic primitive is to attain revocation function and strong privacy guarantee with predicate authentication, where a predicate is a boolean function over attributes of participants. We also give a generic construction of our rADPA scheme. Our approach is to build-in the revocable attribute-based encryption scheme proposed by K.Yamada et al. (ESORICS2017) into the anonymous deniable predicate authentication scheme proposed by S.Yamada et al. (PKC2012). Finally, we discuss how our rADPA scheme can be instantiated by employing concrete building blocks in our generic construction., Journal of Internet Services and Information Security, 11(3), pp.1-15; 2021

This paper explores various relations that exist between replication and trustworthiness. After defining "trust", "trustworthiness", "replicability", "replication study", and "successful replication", we consider, respectively, how trustworthiness relates to each of the three main kinds of replication: reproductions, direct replications, and conceptual replications. Subsequently, we explore how trustworthiness relates to the intentionality of a replication. After that, we discuss whether the trustworthiness of research findings depends merely on evidential considerations or also on what is at stake. We conclude by adding replication to the other issues that should be considered in assessing the trustworthiness of research findings: (1) the likelihood of the findings before the primary study was done (that is, the prior probability of the findings), (2) the study size and the methodological quality of the primary study, (3) the number of replications that were performed and the quality and consistency of their aggregated findings, and (4) what is at stake.

Biometric de-identification is an emerging topic of research within the information security domain that integrates privacy considerations with biometric system development. A comprehensive overview of research in the context of authentication applications spanning physiological, behavioral, and social-behavioral biometric systems and their privacy considerations is discussed. Three categories of biometric de-identification are introduced, namely complete de-identification, auxiliary biometric preserving de-identification, and traditional biometric preserving de-identification. An overview of biometric de-identification in emerging domains such as sensor-based biometrics, social behavioral biometrics, psychological user profile identification, and aesthetic-based biometrics is presented. The article concludes with open questions and provides a rich avenue for subsequent explorations of biometric de-identification in the context of information privacy.

Interdisciplinary exchange of medical datasets between clinicians and engineers is essential for clinical research. Due to the Data Protection Act, which preserves the rights of patients, full anonymization is necessary before any exchange can take place. Due to the continuous improvement of image quality of tomographic datasets, anonymization of patient-specific information is not sufficient. In this work, we present a prototype that allows to reliably obscure the facial features of patient data, thus enabling anonymization of neurological datasets in image space.

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Public trust is paramount for the well functioning of data driven healthcare activities such as digital health interventions, contact tracing or the build-up of electronic health records. As the use of personal data is the common denominator for these healthcare activities, healthcare actors have an interest to ensure privacy and anonymity of the personal data they depend on. Maintaining privacy and anonymity of personal data contribute to the trustworthiness of these healthcare activities and are associated with the public willingness to trust these activities with their personal data. An analysis of online news readership comments about the failed care.data programme in England revealed that parts of the public have a false understanding of anonymity in the context of privacy protection of personal data as used for healthcare management and medical research. Some of those commenting demanded complete anonymity of their data to be willing to trust the process of data collection and analysis. As this demand is impossible to fulfil and trust is built on a false understanding of anonymity, the inability to meet this demand risks undermining public trust. Since public concerns about anonymity and privacy of personal data appear to be increasing, a large-scale information campaign about the limits and possibilities of anonymity with respect to the various uses of personal health data is urgently needed to help the public to make better informed choices about providing personal data., Life Sciences, Society and Policy, 17, ISSN:2195-7819

The approximate graph colouring problem, whose complexity is unresolved in most cases, concerns finding a $c$-colouring of a graph that is promised to be $k$-colourable, where $c\geq k$. This problem naturally generalises to promise graph homomorphism problems and further to promise constraint satisfaction problems. The complexity of these problems has recently been studied through an algebraic approach. In this paper, we introduce two new techniques to analyse the complexity of promise CSPs: one is based on topology and the other on adjunction. We apply these techniques, together with the previously introduced algebraic approach, to obtain new unconditional NP-hardness results for a significant class of approximate graph colouring and promise graph homomorphism problems., Comment: This merges and subsumes arXiv:1904.03214 and arXiv:1907.00872. After reviews

Mobile crowdsourcing (MCS) has become an effective data collection method due to its mobility, low cost, and flexibility. However, since centralized MCS confronts severe security and privacy risks in reality, many researchers are devoted to building a decentralized MCS system based on blockchain. Despite the effectiveness of these schemes, they fail to offer anonymous authentication on the trust of MCS nodes, although privacy is a main concern in MCS and trust plays an important role in a series of MCS activities such as worker selection and truth discovery. Nevertheless, anonymous authentication on trust is not a trivial issue since trust evaluation usually conflicts with anonymity, which is a necessary privacy requirement in an open MCS environment. To tackle this problem, we leverage Intel Software Guard Extension (SGX) and propose a scheme to anonymously authenticate trust with trustworthy trust evaluation in a blockchain-based MCS system. The scheme employs an SGX-enabled cloud server to periodically alter user public/private key pairs and mix newly altered keys among a number of faked keys in order to ensure unlinkability. Besides, we consider the unique features of MCS and work out a novel trust evaluation method by aggregating both subjective feedback and objective behaviors. Finally, we conduct several analyses and experiments to illustrate its security and efficiency.

Encryption provides a method to protect data out-sourced to a DBMS provider, e.g., in the cloud. However, performing database operations over encrypted data requires specialized encryption schemes that carefully balance security and performance. In this paper, we present a new encryption scheme that can efficiently perform equi-joins over encrypted data using only software and a single server with better security than the state-of-the-art. In particular, our encryption scheme reduces the leakage to equality of rows that match a selection criterion and only reveals the transitive closure of the union of the leakages of each query in a series of queries. Our encryption scheme is provable secure. We implemented our encryption scheme and evaluated it over a dataset from the TPC- H benchmark., 2022 IEEE 38th International Conference on Data Engineering (ICDE), May 9-11, 2022, Kuala Lumpur, Malaysia (virtual conference)

This article extends understanding of trust repair by explaining in more detail the dynamics of trust at the network level. Building on organizational-level trust repair research, the article explains how two periods of trust repair – catharsis and catalysis – contribute to trust repair of an interorganizational network. In addition, the article describes how changes to network-level trust in an interorganizational network change the governance form of the network, making the interorganizational network more durable and stable.

When working with user data providing well-defined privacy guarantees is paramount. In this work we aim to manipulate and share an entire sparse dataset with a third party privately. In fact, differential privacy has emerged as the gold standard of privacy, however, when it comes to sharing sparse datasets, as one of our main results, we prove that \emph{any} differentially private mechanism that maintains a reasonable similarity with the initial dataset is doomed to have a very weak privacy guarantee. Hence we need to opt for other privacy notions such as $k$-anonymity are better at preserving utility in this context. In this work we present a variation of $k$-anonymity, which we call smooth $k$-anonymity and design simple algorithms that efficiently provide smooth $k$-anonymity. We further perform an empirical evaluation to back our theoretical guarantees, and show that our algorithm improves the performance in downstream machine learning tasks on anonymized data.

Fingerprint image quality affects heavily the performance of fingerprint recognition systems. This paper reviews existing approaches for fingerprint image quality computation. We also implement, test and compare a selection of them using the MCYT database including 9000 fingerprint images. Experimental results show that most of the algorithms behave similarly., Published at 3rd COST-275 Workshop on Biometrics on the Internet. arXiv admin note: text overlap with arXiv:2111.07432

Refer to our paper to find details about the way it is generated and the dataset structure. "Encrypted Traffic Detection: Beyond the Port Number Era"

Videos are prone to tampering attacks that alter the meaning and deceive the audience. Previous video forgery detection schemes find tiny clues to locate the tampered areas. However, attackers can successfully evade supervision by destroying such clues using video compression or blurring. This paper proposes a video watermarking network for tampering localization. We jointly train a 3D-UNet-based watermark embedding network and a decoder that predicts the tampering mask. The perturbation made by watermark embedding is close to imperceptible. Considering that there is no off-the-shelf differentiable video codec simulator, we propose to mimic video compression by ensembling simulation results of other typical attacks, e.g., JPEG compression and blurring, as an approximation. Experimental results demonstrate that our method generates watermarked videos with good imperceptibility and robustly and accurately locates tampered areas within the attacked version., Submitted to MMSP 2022

A technique for increasing the stability of methods for applying digital watermark into digital images is presented. A technique for increasing the stability of methods for applying digital watermarks into digital images, based on pseudo-holographic coding and additional filtering of a digital watermark, has been developed. The technique described in this work using pseudo-holographic coding of digital watermarks is effective for all types of attacks that were considered, except for image rotation. The paper presents a statistical indicator for assessing the stability of methods for applying digital watermarks. The indicator makes it possible to comprehensively assess the resistance of the method to a certain number of attacks. An experimental study was carried out according to the proposed method. This technique is most effective when part of the image is lost. When pre-filtering a digital watermark, the most effective is the third filtering method, which is averaging over a cell with subsequent binarization. The least efficient is the first method, which is binarization and finding the statistical mode over the cell. For an affine type attack, which is an image rotation, this technique is effective only when the rotation is compensated. To estimate the rotation angle, an affine transformation matrix is found, which is obtained from a consistent set of corresponding ORB-descriptors. Using this method allows to accurately extract a digital watermark for the entire range of angles. A comprehensive assessment of the methodology for increasing the stability of the method of applying a digital watermark based on Wavelet transforms has shown that this method is 20 % better at counteracting various types of attacks

Edge computing has developed rapidly in recent years due to its advantages of low bandwidth overhead and low delay, but it also brings challenges in data security and privacy. Website fingerprinting (WF) is a passive traffic analysis attack that threatens website privacy which poses a great threat to user’s privacy and web security. It collects network packets generated while a user accesses website, and then uses a series of techniques to discover patterns of network packets to infer the type of website user accesses. Many anonymous networks such as Tor can meet the need of hide identity from users in network activities, but they are also threatened by WF attacks. In this paper, we propose a website fingerprinting obfuscation method against intelligent fingerprinting attacks, called Random Bidirectional Padding (RBP). It is a novel website fingerprinting defense technology based on time sampling and random bidirectional packets padding, which can covert the real packets distribution to destroy the Inter-Arrival Time (IAT) features in the traffic sequence and increase the difference between the datasets with random bidirectional virtual packets padding. We evaluate the defense against state-of-the-art website fingerprinting attacks in real scenarios, and show its effectiveness.

Fingerprint authentication has gained attention in recent years due to its distinctiveness, low‐cost sensors, and user willingness to submit them. Its extensive deployment in our automated world raises major concerns regarding the secrecy of biometric templates and the privacy of rightful owners in biometric systems. Fuzzy Commitment (FC) focuses on securing the biometric templates by performing authentication based on the validity of secret keys from biometric features. However, the major challenge in designing Fingerprint‐based FC is the requirement of efficient binary representation for unordered and variable minutiae points in fingerprint images. Additionally, its leakage can compromise an intrinsic characteristic of the individual. The paper proposes Fingerprint‐based FC incorporating an encoding scheme dependent on the number and type of minutiae points present near the fingerprint's core point. The biometric templates are never identical thereby, this approach allows fuzziness in minutiae points by incorporating Bose–Chaudhuri–Hocquenghem (BCH) codes for error‐correction and secures random codeword of the error‐correcting scheme as secret key by SHA‐256 hash mapping. The proposed approach is evaluated on FVC2000‐DB2, FVC2002‐DB1, FVC2002‐DB2, and FVC2004‐DB1, and the results illustrate the efficiency of the proposed scheme. Furthermore, the security analysis of stored helper data and hash mapping demonstrates that the proposed approach is secure.

The extremely vibrant, scattered, and non–transparent nature of cloud computing formulate trust management a significant challenge. According to scholars the trust and security are the two issues that are in the topmost obstacles for adopting cloud computing. Also, SLA (Service Level Agreement) alone is not necessary to build trust between cloud because of vague and unpredictable clauses. Getting feedback from the consumers is the best way to know the trustworthiness of the cloud services, which will help them improve in the future. Several researchers have stated the necessity of building a robust management system and suggested many ideas to manage trust based on consumers' feedback. This paper has reviewed various reputation-based trust management systems, including trust management in cloud computing, peer-to-peer system, and Adhoc system.

Wearable devices have found numerous applications in healthcare ranging from physiological diseases, such as cardiovascular diseases, hypertension and muscle disorders to neurocognitive disorders, such as Parkinson’s disease, Alzheimer’s disease and other psychological diseases. Different types of wearables are used for this purpose, for example, skin-based wearables including tattoo-based wearables, textile-based wearables, and biofluidic-based wearables. Recently, wearables have also shown encouraging improvements as a drug delivery system; therefore, enhancing its utility towards personalized healthcare. These wearables contain inherent challenges, which need to be addressed before their commercialization as a fully personalized healthcare system. This paper reviews different types of wearable devices currently being used in the healthcare field. It also highlights their efficacy in monitoring different diseases and applications of healthcare wearable devices (HWDs) for diagnostic and treatment purposes. Additionally, current challenges and limitations of these wearables in the field of healthcare along with their future perspectives are also reviewed.

Interest in design methods and tools has been steadily growing in HRI. Yet, design is not acknowledged as a discipline with specific epistemology and methodology. Designerly HRI work is validated through user studies which, we argue, provide a limited account of the knowledge design produces. This paper aims to broaden current understanding of designerly HRI work and its contributions by unpacking what designerly knowledge is and how to produce it. Through a critical analysis of current HRI design literature, we identify a lack of work dedicated to understanding the conceptual implications of robotic artifacts. These, in fact, are implicit carriers of crucial HRI knowledge that can challenge established assumptions about how a robot should look, act, and be like. We conclude by discussing a set of practices desirable to legitimize designerly HRI work, and calling for further research addressing the conceptual implications designerly HRI work.

With the rapidly growing use of biometric authentication systems, the security of these systems and the privacy of users have attracted significant attention in recent years. Multi‐modal biometrics have been able to improve the accuracy of the system but require additional bandwidth to exchange the data. Fragile watermarking has been used to allow the transmission of both biometric templates using the amount of data required to transmit one of them, that is, the cover image, while securing these templates against attacks. Despite the high accuracy of these systems, communicating such templates imposes risks towards the privacy of the users. In this study, a new method is proposed to generate fixed‐size descriptors for the face and fingerprint templates, including the timestamp of the transmission and a unique system identifier. The inclusion of the timestamp enables the system to detect and deny replay attacks, while the unique system identifier maintains the privacy of the users. The experiments conducted to evaluate the proposed method have shown that the proposed method has been able to achieve these features while maintaining high recognition rates, 99.41% and 99.32%, similar to the use of the entire biometric templates in the matching stage.

Online education is important in the COVID-19 pandemic, but online exam at individual homes invites students to cheat in various ways, especially collusion. While physical proctoring is impossible during social distancing, online proctoring is costly, compromises privacy, and can lead to prevailing collusion. Here we develop an optimization-based anti-collusion approach for distanced online testing (DOT) by minimizing the collusion gain, which can be coupled with other techniques for cheating prevention. With prior knowledge of student competences, our DOT technology optimizes sequences of questions and assigns them to students in synchronized time slots, reducing the collusion gain by 2–3 orders of magnitude relative to the conventional exam in which students receive their common questions simultaneously. Our DOT theory allows control of the collusion gain to a sufficiently low level. Our recent final exam in the DOT format has been successful, as evidenced by statistical tests and a post-exam survey.

The purpose of this study is to explore trust repair strategies and the outcomes in mobile (m-)commerce. Consumer trust in m-commerce is a calculus-based form of trust, service failures, “inaction” and “excessive actions” are the typical trust decline situations. Three strategies of the fulfillment of consumers’ psychological contract for compensation, arousal, and regulation are proposed as trust-repaired solutions regarding the abovementioned three situations. The experimental results demonstrate that trust cannot be repaired by a single relational psychological contract strategy and that it can be repaired by both a single transactional and interactive strategy, the interactive strategy is more effective in competence-based decline. Second, in integrity-based decline, both the single and interactive strategy negatively influence trust repair, the relational strategy has the strongest effect. The “boomerang effect” shows that the more m-vendors repair, the more the trust decreases. Third, trust can be repaired, respectively by arousal and regulation strategy regarding corresponding situations of inaction and excessive actions. Finally, the trust repair paradox (TRP) has not been verified in m-commerce.

Orthogonal polar image moments which are defined over a unit disk, are used often in watermarking applications. One such image moment is polar harmonic Fourier transformation (PHFT). The quality of the extracted watermark depends on the accuracy of the computed image moments. Various methods have already been used for the computation of the moments with varying degrees of accuracy. This study proposes application of a novel image moments computation method which is a synergy of analytic, and numerical techniques, to image watermarking. The computed moment results in a robust watermark extraction. The PHFT have rotation, and scaling invariance property, which allows the watermark to resist various geometric attacks. In addition to the geometric attacks the watermark has the capability to resist various signal processing attacks. The performance of the proposed watermarking scheme is performed with the methods used in recently published articles. The experimental results show that the proposed method has the better performance compared to other techniques, and has better capability to resist various attacks.

In order to enhance the security of exchanged medical images in telemedicine, we propose in this paper a blind and robust approach for medical image protection. This approach consists in embedding patient information and image acquisition data in the image. This imperceptible integration must generate the least possible distortion. The watermarked image must present the same clinical reading as the original image. The proposed approach is applied in the frequency domain. For this purpose, four transforms were used: discrete wavelets transform, non-subsampled contourlet transform, non-subsampled shearlet transform and discreet cosine transform. All these transforms was combined with Schur decomposition and the watermark bits were integrated in the upper triangular matrix. To obtain a satisfactory compromise between robustness and imperceptibility, the integration was performed in the medium frequencies of the image. Imperceptibility and robustness experimental results shows that the proposed methods maintain a high quality of watermarked images and are remarkably robust against several conventional attacks.

Wearables provide great opportunities for improving personal health, but research challenges their capacity to evoke behavioral change effectively. Realizing the full potential of wearables requires a better understanding of users’ behavior change processes. Based on self-efficacy theory, we investigate how wearables influence users’ perceptions of their self-efficacy and subsequent health behavior. Using narrative interviews with twenty-five long-term wearable users, we show that wearables can have both positive and negative effects on users’ perceptions of their self-efficacy and that these perceptions are subject to internal and external contexts, which can positively or negatively affect users’ compliance. We also find that the internal context may have a compounding or neutralizing effect on self-efficacy, despite an adverse external context. Our study shows the contextual and transient nature of self-efficacy, thus contributing to self-efficacy theory and research on wearables and offering practical design implications.

With the emergence of cloud computing paradigms, secure data outsourcing has become one of the crucial challenges which strongly imposes itself. Data owners place their data among cloud service providers in order to increase flexibility, optimize storage, enhance data manipulation and decrease processing time. Nevertheless, from a security point of view, access control is a major challenge in this situation seeing that the security policy of the data owner must be preserved when data is moved to the cloud. Nonetheless, the lack of a comprehensive and systematic review motivated us to construct this reviewing paper on this research problem. Here, we discuss current and emerging research on privacy and confidentiality concerns in data outsourcing and pinpoint potential issues that are still unresolved.

With the striking development of big data, individual privacy and data security obtain unprecedented importance. Database anonymization mechanism is created for protecting individual privacy by adding noise to the result of a query, which finds a tradeoff between the privacy and utility of personal data. However, corresponding attacks are emerging continuously resulting in a high risk of individual identification. In this paper, we learn patterns of malicious SQL queries and propose a novel detection method. Association rules are used to mine patterns and features of noise-exploitation attacks, and parse trees are applied to the feature extraction of SQL, thereby we construct feature vectors and input them into the classifiers. At the same time, we also propose a SQL generation method to generate query samples based on a real database for model training and testing. Experiments show that our detection method can significantly prevent noise-exploitation attacks including almost all differential attacks and 91% cloning attacks based on the synthetic dataset, which ensures a strong degree of data utility.

Robust watermarking is a valuable methodology used in protecting the copyright and securing digital images. In this paper, new fractional-order multi-channel orthogonal exponent moments (MFrEMs) and their invariants to geometric transformations are derived for the first time. We utilized these highly accurate moments to construct a new robust watermarking algorithm for color images. This algorithm consists of three phases. First, the bits of the binary watermark scrambled by using a 1D Sine chaotic map. Second, the fractional-order MFrEMs are calculated from the host color image. Finally, a quantization process is performed, where the scrambled bits of the binary watermark embedded into the host color image. Various experiments were conducted to test the proposed watermarking algorithm and compare it with the existing robust watermarking algorithms for color images. The obtained results ensure the proposed robust watermarking algorithm’s superiority over existing algorithms regarding the visual imperceptibility and robustness against various attacks.

Smart home is intended to be able to enhance home automation systems and achieves goals such as reducing operational costs and increasing comfort while providing security to mobile users. However, an attacker may attempt security attacks in smart home environments because he/she can inject, insert, intercept, delete, and modify transmitted messages over an insecure channel. Secure and lightweight authentication protocols are essential to ensure useful services in smart home environments. In 2020, Iqbal et al. presented an anonymous lightweight authentication protocol for software-defined networking (SDN) enabled smart home, called ALAM. They claimed that ALAM protocol could resist security threats, and also provide secure mutual authentication and user anonymity. This comment demonstrates that ALAM protocol is fragile to various attacks, including session key disclosure, impersonation, and man-in-the-middle attacks, and also their scheme cannot provide user anonymity and mutual authentication. We propose the essential security guidelines to overcome the security flaws of ALAM protocol.

Anonymization is a practical solution for preserving user’s privacy in data publishing. Data owners such as hospitals, banks, social network (SN) service providers, and insurance companies anonymize their user’s data before publishing it to protect the privacy of users whereas anonymous data remains useful for legitimate information consumers. Many anonymization models, algorithms, frameworks, and prototypes have been proposed/developed for privacy preserving data publishing (PPDP). These models/algorithms anonymize users’ data which is mainly in the form of tables or graphs depending upon the data owners. It is of paramount importance to provide good perspectives of the whole information privacy area involving both tabular and SN data, and recent anonymization researches. In this paper, we presents a comprehensive survey about SN (i.e., graphs) and relational (i.e., tabular) data anonymization techniques used in the PPDP. We systematically categorize the existing anonymization techniques into relational and structural anonymization, and present an up to date thorough review on existing anonymization techniques and metrics used for their evaluation. Our aim is to provide deeper insights about the PPDP problem involving both graphs and tabular data, possible attacks that can be launched on the sanitized published data, different actors involved in the anonymization scenario, and major differences in amount of private information contained in graphs and relational data, respectively. We present various representative anonymization methods that have been proposed to solve privacy problems in application-specific scenarios of the SNs. Furthermore, we highlight the user’s re-identification methods used by malevolent adversaries to re-identify people uniquely from the privacy preserved published data. Additionally, we discuss the challenges of anonymizing both graphs and tabular data, and elaborate promising research directions. To the best of our knowledge, this is the first work to systematically cover recent PPDP techniques involving both SN and relational data, and it provides a solid foundation for future studies in the PPDP field.

In order to improve the security and efficiency of image encryption, many researchers have continuously proposed new image encryption schemes in recent years. However, these image encryption schemes have not been fully analyzed and evaluated. In this paper, a newly reported image encryption scheme based on Feistel network and dynamic Deoxyribonucleic Acid (DNA) encoding is deeply and comprehensively investigated. This encryption scheme mainly adopts four encryption steps to encrypt the plain image, which are Generation of chaotic sequences, Hill encryption, Feistel network, and Pixel diffusion. Our analyses show that there are some problems in the secret key design and encryption process of this encryption scheme. After pointing out and analyzing these problems, we have made several necessary improvements to this encryption scheme and proposed the corresponding chosen-plaintext attack algorithm. The subsequent simulation tests and analyses have confirmed the effectiveness and feasibility of the proposed attack algorithm. Finally, for the problems in this encryption scheme and some current image encryption schemes, improvement suggestions are presented to provide references for the designers of future image encryption schemes.