Level Of Privacy Protection Research Articles

Phishing as a form of fraud identity theft in healthcare: Victimization during COVID-19 pandemic

The coronavirus pandemic affects all segments of human life around the world. Due to the risk to people's health, personal communication has been completely changed and most professional activities and contacts must take place in a cyber environment, with the help of computers and mobile telephony. On-line communication and the use of social networks undoubtedly represent a conditio sine qua non in a situation when compliance with numerous preventive measures are required, including the work from home, online teaching in schools, a ban on gatherings etc. The positive aspects of this kind of communication are undoubtedly obvious and necessary, but they also created new forms of victimization due to insecure protocols for sending e-mails, inadequate level of privacy protection, insufficient information security, the existence of so-called security holes and the use of the same devices and digital services for professional and private purposes. Hacking phishing activities during the pandemic mostly endangered the security of confidential data collected within medical information systems, educational institutions, and in the field of banking, when the user is expected to compromise his/her digital identity and publish his/her data. The subject of this paper is the presentation of some of the most common phishing activities and identity theft in the field of health care, provision of health services, procurement of medicines and vaccines against coronavirus, which have been recorded in the world since the beginning of the pandemic. The phishing activities that will be presented in this paper significantly contribute to the further spread of panic from the misuse of medical data of infected persons and their contacts. The paper aims to point out the most common forms of victimization that have appeared around the world, as a consequence of cyber security violations due to hacker attacks during the COVID- 19 pandemic. Special emphasis in the paper is on these ?pandemic? cyber dangers, as well as on the mechanisms for avoiding this type of victimization.

Towards Privacy-Preserving Spatial Distribution Crowdsensing: A Game Theoretic Approach

Acquiring the spatial distribution of users in mobile crowdsensing (MCS) brings many benefits to users ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.,</i> avoiding crowded areas during the COVID-19 pandemic). Although the leakage of users’ location privacy has received a lot of research attention, existing works still ignore the rationality of users, resulting that users may not obtain satisfactory spatial distribution even if they provide true location information. To solve the problem, we employ game theory with incomplete information to model the interactions among users and seek an equilibrium state through learning approaches of the game. Specifically, we first model the service as a game in the satisfaction form and define the equilibrium for this service. Then, we design a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LEFS</i> algorithm for the privacy strategy learning of users when their satisfaction expectations are fixed, and further design <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LSRE</i> that allows users to have dynamic satisfaction expectations. We theoretically analyze the convergence conditions and characteristics of the proposed algorithms, along with the privacy protection level obtained by our solution. We conduct extensive experiments to show the superiority and various performances of our proposal, which illustrates that our proposal can get more than 85% advantage in terms of the sensing distribution availability compared to the traditional spatial cloaking based solutions.

How Do Socio-Demographic Patterns Define Digital Privacy Divide?

Digital privacy has become an essential component of information and communications technology (ICT) systems. There are many existing methods for digital privacy protection, including network security, cryptography, and access control. However, there is still a gap in the digital privacy protection levels available for users. This paper studies the digital privacy divide (DPD) problem in ICT systems. First, we introduce an online DPD study for understanding the DPD problem by collecting responses from 776 ICT users using crowdsourcing task assignments. Second, we propose a factor analysis-based statistical method for generating the DPD index from a set of observable DPD question variables. In particular, the DPD index provides one scaled measure for the DPD gap by exploring the dimensionality of the eight questions in the DPD survey. Third, we introduce a DPD proportional odds model for analyzing the relationship between the DPD status and the socio-demographic patterns of the users. Our results show that the DPD survey meets the internal consistency reliability with rigorous statistical measures, e.g., Cronbach's $\alpha=0.92$. Furthermore, the DPD index is shown to capture the underlying communality of all DPD variables. Finally, the DPD proportional odds model indicates a strong statistical correlation between the DPD status and the age groups of the ICT users. For example, we find that young users (15-32 years) are generally more concerned about their digital privacy than senior ones (33 years and over).

Online information privacy and price: A theoretical model and empirical tests

Information privacy has become an increasingly critical concern for many individuals, especially in the context of the increased collection, analysis, and visualization of personal information. Despite the importance of privacy in online transactions, most studies investigated the effects of privacy concerns on stated intentions. This study theoretically analyzes and empirically evaluates the link between privacy and price. In the proposed model, consumers are differentiated not only by their preferences for privacy but also by search costs. For simplicity, stores in the proposed model differ in terms of the price charged and the privacy protection level offered. As derived from the theoretical model and empirical validation, better privacy protection does not necessarily lead to the highest price, though it does allow stores to charge a premium price. Moreover, the price premium is higher if more consumers remain uninformed, indicating that uninformed consumers exert negative impacts on informed consumers.

A Blockchain-Integrated Divided-Block Sparse Matrix Transformation Differential Privacy Data Publishing Model

With the rapid development of information technology, people benefit more and more from big data. At the same time, it becomes a great concern that how to obtain optimal outputs from big data publishing and sharing management while protecting privacy. Many researchers seek to realize differential privacy protection in massive high-dimensional datasets using the method of principal component analysis. However, these algorithms are inefficient in processing and do not take into account the different privacy protection needs of each attribute in high-dimensional datasets. To address the above problem, we design a Divided-block Sparse Matrix Transformation Differential Privacy Data Publishing Algorithm (DSMT-DP). In this algorithm, different levels of privacy budget parameters are assigned to different attributes according to the required privacy protection level of each attribute, taking into account the privacy protection needs of different levels of attributes. Meanwhile, the use of the divided-block scheme and the sparse matrix transformation scheme can improve the computational efficiency of the principal component analysis method for handling large amounts of high-dimensional sensitive data, and we demonstrate that the proposed algorithm satisfies differential privacy. Our experimental results show that the mean square error of the proposed algorithm is smaller than the traditional differential privacy algorithm with the same privacy parameters, and the computational efficiency can be improved. Further, we combine this algorithm with blockchain and propose an Efficient Privacy Data Publishing and Sharing Model based on the blockchain. Publishing and sharing private data on this model not only resist strong background knowledge attacks from adversaries outside the system but also prevent stealing and tampering of data by not-completely-honest participants inside the system.

An Energy-Efficient Authentication Scheme Based on Chebyshev Chaotic Map for Smart Grid Environments

Electric vehicle charging is becoming more commonplace, but a number of challenges remain. For example, the wireless communications between vehicle users and aggregators can be subject to exploitation and hence, several authentication schemes have been designed to support varying levels of privacy protection. However, there are a number of limitations observed in existing authentication schemes, and examples include lack of anonymity and not considering charging peak in their design (and consequently, not meeting low energy consumption requirement in smart grid environments). More recently, there have been attempts to utilize Chevyshev chaotic map in the design of authentication mechanism, with the aims of reducing computational costs yet achieving high security. However, the security requirements of Chebyshev polynomials pose new challenges to the construction of Chebyshev chaotic maps-based authentication schemes. To solve these limitations, we propose an efficient Chebyshev polynomials algorithm by adopting a square matrix-based binary exponentiation algorithm to provide secure and efficient Chebyshev polynomial computation. We further construct an energy-efficient authentication and key negotiation scheme for the smart grid environments based on the proposed algorithm. Compared with five other competing schemes, our proposed authentication scheme achieves reduced computational and communication costs. In addition, the ProVerif tool is used to analyze the security of our proposed authentication scheme. The results show that the proposed scheme outperforms these five other schemes in terms of computation and communication overheads while achieving privacy preserving.

Privacy-preserving location-based traffic density monitoring

Traffic density monitoring is an important method to predict road traffic conditions, which can bring some convenience to people's travel in daily life. The common method of traffic density monitoring is to collect and process the location information uploaded by vehicles, but the information of these vehicle location contains a large amount of personal privacy information of vehicle owners, and there is a risk of privacy disclosure. In this paper, we propose a traffic density monitoring system by adding a pseudonym server and a location anonymisation server; the identity information and location information of the vehicles are saved separately. The system can protect both the location privacy of vehicles and the query privacy of users. To prevent dummy locations from being filtered, we calculate the probability distribution of historical location service requests to generate location anonymous sets, which can improve the success rate of anonymity. The location anonymisation server uses the location anonymous set instead of the real location of the vehicle to send to the location-based service provider, which can increase the location privacy security of the vehicle. According to the experimental results of this paper, compared with SimpMaxMinDistds algorithm and MMDS algorithm, our system has better location anonymous set generation efficiency and location privacy protection level.

Utility-preserving differentially private skyline query

Skyline query has been widely applied in multi-criterion decision making, whose potential privacy risk is practically concerned on some specific occasions. Differential Privacy (DP) is a rigorous privacy-preserving method for its robustness and reliability. Considering DP will deteriorate the data utility, this paper proposes the Individual Differential Privacy via Spectral Clustering (iDP-SC) to address the privacy leakage in skyline query. It shifts the calculation of the local sensitivity from the original dataset to the one processed by spectral clustering, and through this, the sensitivity is reduced as well as the calibrated noise. As a result, it maintain higher utility without sacrificing the privacy preservation provided by differential privacy. Furthermore, compared with existing work for privacy-preserving skyline query, the proposed iDP-SC avoids the disclosure of key information, while providing the quantitative analysis of privacy protection level. The performance of the proposed iDP-SC was examined through comparison with the DP and Individual Differential Privacy (iDP) on both real and synthetic datasets. The experimental results demonstrates the capability and effectiveness of the proposed approach. Along this line of research, future work could be extended to the situation where the curator is un-trusted, or the personalized privacy requirements of retailers etc.

Privacy-preserving Byzantine-robust federated learning

Robustness of federated learning has become one of the major concerns since some Byzantine adversaries, who may upload false data owning to unreliable communication channels, corrupted hardware or even malicious attacks, might be concealed in the group of the distributed worker. Meanwhile, it has been proved that membership attacks and reverse attacks against federated learning can lead to privacy leakage of the training data. To address the aforementioned challenges, we propose a privacy-preserving Byzantine-robust federated learning scheme (PBFL) which takes both the robustness of federated learning and the privacy of the workers into account. PBFL is constructed from an existing Byzantine-robust federated learning algorithm and combined with distributed Paillier encryption and zero-knowledge proof to guarantee privacy and filter out anomaly parameters from Byzantine adversaries. Finally, we prove that our scheme provides a higher level of privacy protection compared to the previous Byzantine-robust federated learning algorithms.

An enhanced symmetric-key based 5G-AKA protocol

5G technology is called to support the next generation of wireless communications and realize the “Internet of Everything” through its mMTC (massive Machine-Type-Communications) service. The recently standardized 5G-AKA protocol is intended to deal with security and privacy issues detected in earlier generations. Nevertheless, several 5G-AKA shortcomings have been reported, including a possibly excessive computational complexity for many IoT devices. To address these, a promising lightweight 2-pass authentication and key agreement (AKA) protocol for 5G mobile communications has recently been proposed by Braeken. Compared to the 5G-AKA protocol, this does not require the use of public key encryption. This paper analyzes the security claims of Braeken’s protocol and shows that it does not provide full unlinkability, but only session unlinkability, and is (still) subject to Linkability of AKA Failure Messages (LFM) attacks. We propose solutions to such problems and prove that symmetric-key based protocols cannot offer higher privacy protection levels without compromising availability. We then describe an enhanced version of this protocol that addresses these vulnerabilities and supports forward secrecy, which is a desirable feature for low-cost IoT devices.

Artificial Intelligence in Health Care: Trust, Privacy, and the Acceptance of AI Doctors

Artificial intelligence is revolutionizing many industries, including the health care industry. While certain artificial intelligence applications are already being used to assist doctors with specific tasks, the development of artificial intelligence doctors that can autonomously interact with patients is still in an early phase. Such systems could potentially address several challenges that our health care systems are facing today, but patients are currently still resisting medical artificial intelligence. This paper investigates how trust and privacy impact the acceptance of such artificial intelligence doctors. Using data collected in an online questionnaire, this paper presents evidence that perceived risk, trusting beliefs, and perceived benefits play an important role in the formation of intentions to use artificial intelligence doctors. Trusting beliefs and perceived risk can be manipulated through the provision of trust forming information and the level of privacy protection that is offered by these artificial intelligence doctors. It is further shown that individuals’ privacy literacy influences their ability to adequately assess risks associated with those privacy protection measures. This paper lays the groundwork for future research on the acceptance of artificial intelligence doctors and provides valuable insights for developers of such technology.

State of the Art in Adoption of Contact Tracing Apps and Recommendations Regarding Privacy Protection and Public Health: Systematic Review.

BackgroundDuring the COVID-19 pandemic, contact tracing apps have received a lot of public attention. The ongoing debate highlights the challenges of the adoption of data-driven innovation. We reflect on how to ensure an appropriate level of protection of individual data and how to maximize public health benefits that can be derived from the collected data.ObjectiveThe aim of the study was to analyze available COVID-19 contact tracing apps and verify to what extent public health interests and data privacy standards can be fulfilled simultaneously in the process of the adoption of digital health technologies.MethodsA systematic review of PubMed and MEDLINE databases, as well as grey literature, was performed to identify available contact tracing apps. Two checklists were developed to evaluate (1) the apps’ compliance with data privacy standards and (2) their fulfillment of public health interests. Based on both checklists, a scorecard with a selected set of minimum requirements was created with the goal of estimating whether the balance between the objective of data privacy and public health interests can be achieved in order to ensure the broad adoption of digital technologies.ResultsOverall, 21 contact tracing apps were reviewed. In total, 11 criteria were defined to assess the usefulness of each digital technology for public health interests. The most frequently installed features related to contact alerting and governmental accountability. The least frequently installed feature was the availability of a system of medical or organizational support. Only 1 app out of 21 (5%) provided a threshold for the population coverage needed for the digital solution to be effective. In total, 12 criteria were used to assess the compliance of contact tracing apps with data privacy regulations. Explicit user consent, voluntary use, and anonymization techniques were among the most frequently fulfilled criteria. The least often implemented criteria were provisions of information about personal data breaches and data gathered from children. The balance between standards of data protection and public health benefits was achieved best by the COVIDSafe app and worst by the Alipay Health Code app.ConclusionsContact tracing apps with high levels of compliance with standards of data privacy tend to fulfill public health interests to a limited extent. Simultaneously, digital technologies with a lower level of data privacy protection allow for the collection of more data. Overall, this review shows that a consistent number of apps appear to comply with standards of data privacy, while their usefulness from a public health perspective can still be maximized.

Privacy-Preserving Information Sharing within an Audit Firm

ABSTRACTThis paper explores the possibility of sharing contemporaneous firm-level information within an audit firm in a privacy-preserving manner. We develop a number of sharing schemes for utilizing contemporaneous accounting information from peer companies without violating clients' confidentiality and observe significant improvements in both estimation accuracy and error detection performance. To satisfy different levels of privacy protection, we propose different sharing schemes by utilizing auditors' self-generated expectations, and the results show that the benefits to auditors from only sharing self-generated estimation residuals (errors) are comparable to that from sharing predicted or actual accounting numbers. To satisfy stricter privacy concerns, we also propose a series of schemes based on sharing categorical information derived from prediction errors. Finally, we use Borda counts to analyze how the choice of the best model changes depending on the cost of errors within different experimental settings.

A survey on intrusion detection system and prerequisite demands in IoT networks

Over the past few years the development in the mobile industry and development of internet, network for all, 4G, 5G etc. enabled the ordinary people as well as the elite people to depend upon mobile networks for regular business developments, entertainment, medical and educational needs. Almost all areas of development depend on the so called improvement of the mobile network. As the advantages and flexibility increases, the consumers entering by new registration increase widely and service requirement of existing consumers increase massively. It is mandatory to provide high level of security and dual privacy protection to the users sharing the large set of information through the cloud. The massive crowd sensing is important for any kind of network security system to ensure the detection of any miscellaneous activity entering the network grid. The study is focused on gathering various literature evidences on demand for intrusion detection system, analyzing the pitfalls in current models and creating an idea that would be helpful for us to proceed further with the research on intrusion detection system implementations and innovating a novel methodology that improvise from the present system. The future enhancement and interpretations on solutions would be discussed too.

Differentially private and utility-aware publication of trajectory data

Trajectory data is valuable for various applications, especially for intelligent transportation systems, which hunger for plenty of trajectories. However, publishing trajectory data while respecting users’ privacy has been a long-standing challenge. Currently, the prevailing releasing solutions usually merge trajectory locations based on k-means and add unbounded noise with Laplace distribution to the count of trajectory to achieve differential privacy protection. The trajectory merging methods based on k-means have a low efficiency and unbounded noise with Laplace distribution will leak user’ privacy and suffer from serious utility loss. To solve the above two problems, we devise two differentially private and utility-aware publication methods of trajectory data. More specifically, we first propose two trajectory merging schemes based on k-means || clustering. The first one is to use k-means || clustering algorithm to cluster the location area, and all the points in the cluster are replaced by the center of cluster. The other is to utilize Staircase mechanism to perturb the cluster centers in order to improve the level of privacy protection. Afterwards, we propose a bounded Staircase noise generation algorithm to perturb the true count of generalized trajectories. We prove our proposed methods preserve differential privacy theoretically. Experimental comparison show that our proposed publication methods significantly outperform existing approaches in terms of data utility and efficiency.

A Personalized Secure Publishing Mechanism of the Sensing Location Data in Crowdsensing Location-Based Services

Crowdsensing enables extensive data collection for Location-Based Services (LBS). However, there is a large amount of sensitive private information within the sensing data of the user’s location. Hence, it will pose a huge threat to user’s privacy if the sensing locations are published without protection. As usual, the user’s location has two typical attributes, i.e., a geographic one and a social semantic one. Hence, the secure publishing of the user’s sensing location data should consider how to protect both of the attributes-related privacy security. However, even for the same location, the same user may have different privacy protection needs along with different sensitive events, let alone that different users have different privacy protection needs due to their differences in age, gender, occupation, etc. Therefore, we propose a personalized secure publishing mechanism of the sensing location data. At first, it makes collaborative protection between semantic and geographic attribute-related privacy from two aspects, i.e., adjusting the user’s location information loss to meet different privacy protection needs of users, and personalizing the privacy-attack tolerance degree to achieve different privacy protection levels. Then, it optimizes the collaborative protection effects with the Stackelberg game to achieve the best balance between user data security and data quality. The experiments based on the real-world dataset demonstrate the effectiveness of our proposed mechanism.

DP-QIC: A differential privacy scheme based on quasi-identifier classification for big data publication

With the advent of the era of big data, data privacy protection has become a valuable topic in the field of data publication. Unfortunately, traditional methods of privacy protection, k-anonymity, and its extensions are not absolutely secure as an adversary with background knowledge can determine the owner of a record. The emergence of differential privacy provides a reasonable alternative for privacy security, but the existing solutions ignore the correlation between sensitive attributes and other attributes. In this paper, we propose a new differential privacy scheme based on quasi-identifier classification for big data publication (DP-QIC). It is a new data publishing scheme based on the obfuscation of attribute correlation. We innovatively present quasi-identifier classification based on sensitive attributes and the privacy ratio for evaluating the data set vulnerability. DP-QIC achieves data privacy-protecting through four steps: data collection, grouping and shuffling, generalization, merging, and noise adding, which retains the overall statistical characteristics of the data set. Moreover, the exponential mechanism and the Laplace mechanism are integrated to ensure higher flexibility and a stronger level of privacy protection, so DP-QIC can be used for privacy processing of different data groups in future development. Finally, we have compared the performance of our scheme with the other two famous schemes in the industry. Experimental results demonstrate that DP-QIC has obvious advantages in data utility, privacy protection, and processing efficiency.

Mobile Crowdsourcing Task Allocation with Differential-and-Distortion Geo-Obfuscation

In mobile crowdsourcing, organizers usually need participants’ precise locations for optimal task allocation, e.g., minimizing selected workers’ travel distance to task locations. However, the exposure of users’ locations raises privacy concerns. In this paper, we propose a location privacy-preserving task allocation framework with geo-obfuscation to protect users’ locations during task assignments. More specifically, we make participants obfuscate their reported locations under the guarantee of two rigorous privacy-preserving schemes, differential and distortion privacy, without the need to involve any third-party trusted entity. In order to achieve optimal task allocation with the differential-and-distortion geo-obfuscation, we formulate a mixed-integer non-linear programming problem to minimize the expected travel distance of the selected workers under the constraints of differential and distortion privacy. Moreover, a worker may be willing to accept multiple tasks, and a task organizer may be concerned with multiple utility objectives such as task acceptance ratio in addition to travel distance. Against this background, we also extend our solution to the multi-task allocation and multi-objective optimization cases. Evaluation results on both simulation and real-world user mobility traces verify the effectiveness of our framework. Particularly, our framework outperforms Laplace obfuscation, a state-of-the-art geo-obfuscation mechanism, by achieving up to 47 percent shorter average travel distance on real-world data under the same level of privacy protection.

Personalized Location Privacy Protection Based on Vehicle Movement Regularity in Vehicular Networks

Currently, the issue of location privacy has been attracting increasing attention. In traditional pseudonym-based solutions, the pseudonym age of each vehicle increases at the same rate and vehicles change their pseudonyms when they meet vehicles, which also want to change pseudonyms at the same location. However, we observe that according to the individual characteristics of each vehicle’s owner, the movement of each vehicle is regular, i.e., there are some locations that vehicles visit frequently, and some locations that they rarely visit. Evidently, pseudonym change strategies for these locations are different. To address these issues, we propose a sensitivity-based pseudonym change mechanism, which can take full advantage of the regularity of a vehicle’s movement, thereby achieving personalized location privacy. A pseudonym age analytical model is used to quantify the achieved location privacy. Furthermore, we propose a new metric to measure the level of personalized location privacy protection. The results of the performance evaluation demonstrate that our approach significantly outperforms existing approaches in realizing personalized location privacy.

Reconciliation of anti-money laundering instruments and European data protection requirements in permissionless blockchain spaces

AbstractThis article is an attempt to reconcile the requirements of the EU General Data Protection Regulation (GDPR) and anti-money laundering and combat terrorist financing (AML/CFT) instruments used in permissionless ecosystems based on distributed ledger technology (DLT). Usually, analysis is focused only on one of these regulations. Covering by this research the interplay between both regulations reveals their incoherencies in relation to permissionless DLT. The GDPR requirements force permissionless blockchain communities to use anonymization or, at the very least, strong pseudonymization technologies to ensure compliance of data processing with the GDPR. At the same time, instruments of global AML/CFT policy that are presently being implemented in many countries following the recommendations of the Financial Action Task Force, counteract the anonymity-enhanced technologies built into blockchain protocols. Solutions suggested in this article aim to induce the shaping of permissionless DLT-based networks in ways that at the same time would secure the protection of personal data according to the GDPR rules, while also addressing the money laundering and terrorist financing risks created by transactions in anonymous blockchain spaces or those with strong pseudonyms. Searching for new policy instruments is necessary to ensure that governments do not combat the development of all privacy-blockchains so as to enable a high level of privacy protection and GDPR-compliant data processing. This article indicates two AML/CFT tools which may be helpful for shaping privacy-blockchains that can enable the feasibility of such tools. The first tool is exceptional government access to transactional data written on non-transparent ledgers, obfuscated by advanced anonymization cryptography. The tool should be optional for networks as long as another effective AML/CFT measures are accessible for the intermediaries or for the government in relation to a given network. If these other measures are not available and the network does not grant exceptional access, the regulations should allow governments to combat the development of those networks. Effective tools in that scope should target the value of privacy-cryptocurrency, not its users. Such tools could include, as a tool of last resort, state attacks which would undermine the trust of the community in a specific network.