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This paper proposes a generic anonymization approach for person-specific data, which retains more information for data mining and analytical purposes while providing considerable privacy. The proposed approach takes into account the usefulness and uncertainty of attributes while anonymizing the data to significantly enhance data utility. We devised a method for determining the usefulness weight for each attribute item in a dataset, rather than manually deciding (or assuming based on domain knowledge) that a certain attribute might be more useful than another. We employed an information theory concept for measuring the uncertainty regarding sensitive attribute's value in equivalence classes to prevent unnecessary generalization of data. A flexible generalization scheme that simultaneously considers both attribute usefulness and uncertainty is suggested to anonymize person-specific data. The proposed methodology involves six steps: primitive analysis of the dataset, such as analyzing attribute availability in the data, arranging the attributes into relevant categories, and sophisticated pre-processing, computing usefulness weights of attributes, ranking users based on similarities, computing uncertainty in sensitive attributes (SAs), and flexible data generalization. Our methodology offers the advantage of retaining higher truthfulness in data without losing guarantees of privacy. Experimental analysis on two real-life benchmark datasets with varying scales, and comparisons with prior state-of-the-art methods, demonstrate the potency of our anonymization approach. Specifically, our approach yielded better performance on three metrics, namely accuracy, information loss, and disclosure risk. The accuracy and information loss were improved by restraining heavier anonymization of data, and disclosure risk was improved by preserving higher uncertainty in the SA column. Lastly, our approach is generic and can be applied to any real-world person-specific tabular datasets encompassing both demographics and SAs of individuals. [ABSTRACT FROM AUTHOR]

The social Internet of Medical Things (S-IoMT) highly demands dependable and non-invasive device identification and authentication and makes data services more prevalent in a reliable learning system. In real time, healthcare systems consistently acquire, analyze, and transform a few operational intelligence into actionable forms through digitization to capture the sensitive information of the patient. Since the S-IoMT tries to distribute health-related services using IoT devices and wireless technologies, protecting the privacy of data and security of the device is so crucial in any eHealth system. To fulfill the design objectives of eHealth, smart sensing technologies use built-in features of social networking services. Despite being more convenient in its potential use, a significant concern is a security preventing potential threats and infringement. Thus, this paper presents a lightweight two-factor authentication framework (L2FAK) with privacy-preserving functionality, which uses a mobile sink for smart eHealth. Formal and informal analyses prove that the proposed L2FAK can resist cyberattacks such as session stealing, message modification, and denial of service, guaranteeing device protection and data integrity. The learning analysis verifies the features of the physical layer using federated learning layered authentication (FLLA) to learn the data characteristics by exploring the learning framework of neural networks. In the evaluation, the core scenario is implemented on the TensorFlow Federated framework to examine FLLA and other relevant mechanisms on two correlated datasets, namely, MNIST and FashionMNIST. The analytical results show that the proposed FLLA can analyze the protection of privacy features effectively in order to guarantee an accuracy ≈ 89.83 % to 93.41 % better than other mechanisms. Lastly, a real-time testbed demonstrates the significance of the proposed L2FAK in achieving better quality metrics, such as transmission efficiency and overhead ratio than other state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Owning to the massive growth in internet connectivity, smartphone technology, and digital tools, the use of various online social networks (OSNs) has significantly increased. On the one hand, the use of OSNs enables people to share their experiences and information. On the other hand, this ever-growing use of OSNs enables adversaries to launch various privacy attacks to compromise users' accounts as well as to steal other sensitive information via statistical matching. In general, a privacy attack is carried out by the exercise of linking personal data available on the OSN site and social graphs (or statistics) published by the OSN service providers. The problem of securing user personal information for mitigating privacy attacks in OSNs environments is a challenging research problem. Recently, many privacy-preserving solutions have been proposed to secure users' data available over OSNs from prying eyes. However, a systematic overview of the research dynamics of OSN privacy, and findings of the latest privacy-preserving approaches from a broader perspective, remain unexplored in the current literature. Furthermore, the significance of artificial intelligence (AI) techniques in the OSN privacy area has not been highlighted by previous research. To cover this gap, we present a comprehensive analysis of the state-of-the-art solutions that have been proposed to address privacy issues in OSNs. Specifically, we classify the existing privacy-preserving solutions into two main categories: privacy-preserving graph publishing (PPGP) and privacy preservation in application-specific scenarios of the OSNs. Then, we introduce a high-level taxonomy that encompasses common as well as AI-based privacy-preserving approaches that have proposed ways to combat the privacy issues in PPGP. In line with these works, we discuss many state-of-the-art privacy-preserving solutions that have been proposed for application-specific scenarios (e.g., information diffusion, community clustering, influence analysis, friend recommendation, etc.) of OSNs. In addition, we discuss the various latest de-anonymization methods (common and AI-based) that have been developed to infer either identity or sensitive information of OSN users from the published graph. Finally, some challenges of preserving the privacy of OSNs (i.e., social graph data) from malevolent adversaries are presented, and promising avenues for future research are suggested. [ABSTRACT FROM AUTHOR]

Introduction: Recently, the tendency of artificial intelligence (AI) and big data use/applications has been rapidly expanding across the globe, improving people's lifestyles with data-driven services (i.e., recommendations, smart healthcare, etc.). The synergy between AI and big data has become imperative considering the drastic growth in personal data stemming from diverse sources (cloud computing, IoT, social networks, etc.). However, when data meet AI at some central place, it invites unimaginable privacy issues, and one of those issues is group privacy. Despite being the most significant problem, group privacy has not yet received the attention of the research community it is due. Problem Statement: We study how to preserve the privacy of particular groups (a community of people with some common attributes/properties) rather than an individual in personal data handling (i.e., sharing, aggregating, and/or performing analytics, etc.), especially when we talk about groups purposely made by two or more people (with clear group identifying markers), for whom we need to protect their privacy as a group. Aims/Objectives: With this technical letter, our aim is to introduce a new dimension of privacy (e.g., group privacy) from technical perspectives to the research community. The main objective is to advocate the possibility of group privacy breaches when big data meet AI in real-world scenarios. Methodology: We set a hypothesis that group privacy (extracting group-level information) is a genuine problem, and can likely occur when AI-based techniques meet high dimensional and large-scale datasets. To prove our hypothesis, we conducted a substantial number of experiments on two real-world benchmark datasets using AI techniques. Based on the experimental analysis, we found that the likelihood of privacy breaches occurring at the group level by using AI techniques is very high when data are sufficiently large. Apart from that, we tested the parameter effect of AI techniques and found that some parameters' combinations can help to extract more and fine-grained data about groups. Findings: Based on experimental analysis, we found that vulnerability of group privacy can likely increase with the data size and capacity of the AI method. We found that some attributes of people can act as catalysts in compromising group privacy. We suggest that group privacy should also be given due attention as individual privacy is, and robust tools are imperative to restrict implications (i.e., biased decision making, denial of accommodation, hate speech, etc.) of group privacy. Significance of results: The obtained results are the first step towards responsible data science, and can pave the way to understanding the phenomenon of group privacy. Furthermore, the results contribute towards the protection of motives/goals/practices of minor communities in any society. Concluding statement: Due to the significant rise in digitation, privacy issues are mutating themselves. Hence, it is vital to quickly pinpoint emerging privacy threats and suggest practical remedies for them in order to mitigate their consequences on human beings. [ABSTRACT FROM AUTHOR]

With the development of intelligent vehicles, in-vehicle security and privacy receive considerable attention from researchers and drivers. The data stored in a vehicle control unit (VCU) may contain privacy-sensitive content, which may be exposed to unauthorized devices or attackers due to the structural problems of the in-vehicle network. To overcome this problem, data can be encrypted with the target module's key before being transmitted to the in-vehicle network. However, this may lead to another efficiency problem. In this study, we propose a model that adopts the proxy re-encryption (PRE) scheme to the in-vehicle network. This is called privacy-enhancing in-vehicle network key management (PINK). In the proposed model, the data stored in the VCU is encrypted with a master key. In addition, when the stored data is requested by a node, it is transformed into a ciphertext that can only be decrypted with the node's key using the PRE scheme. Moreover, for the case in which a special group of nodes request the data several times, simultaneously, a group-based key management scheme i provided. Further it is demonstrated using simulations that the proposed PINK scheme can be efficiently applied to the in-vehicle network. [ABSTRACT FROM AUTHOR]

Predicate encryption (PE) that provides both the access control of ciphertexts and the privacy of ciphertexts is a new paradigm of public-key encryption. An important application of PE is a searchable encryption system in cloud storage, where it enables a client to securely outsource the search of a keyword on encrypted data without revealing the keyword to the cloud server. One practical issue of PE is to devise an efficient revocation method to revoke a user when the secret key of the user is compromised. Privacy preserving revocable PE (RPE) can provide not only revocation but also the privacy of revoked users. In this paper, we first define two new security models of privacy preserving RPE: the strongly full-hiding (FH) security and the weakly FH security. Next, we propose a general RPE construction from any PE scheme and prove its security in the weakly FH security model. Our generic RPE scheme is efficient because the number of ciphertext elements is not proportional to the number of users in a receiver set. Additionally, our RPE scheme can support polynomial-size circuits if a recently proposed functional encryption scheme for polynomial-size circuits is used as an underlying PE scheme. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper presents the role of artificial intelligence (AI) and other latest technologies that were employed to fight the recent pandemic (i.e., novel coronavirus disease-2019 (COVID-19)). These technologies assisted the early detection/diagnosis, trends analysis, intervention planning, healthcare burden forecasting, comorbidity analysis, and mitigation and control, to name a few. The key-enablers of these technologies was data that was obtained from heterogeneous sources (i.e., social networks (SN), internet of (medical) things (IoT/IoMT), cellular networks, transport usage, epidemiological investigations, and other digital/sensing platforms). To this end, we provide an insightful overview of the role of data-driven analytics leveraging AI in the era of COVID-19. Specifically, we discuss major services that AI can provide in the context of COVID-19 pandemic based on six grounds, (i) AI role in seven different epidemic containment strategies (a.k.a non-pharmaceutical interventions (NPIs)), (ii) AI role in data life cycle phases employed to control pandemic via digital solutions, (iii) AI role in performing analytics on heterogeneous types of data stemming from the COVID-19 pandemic, (iv) AI role in the healthcare sector in the context of COVID-19 pandemic, (v) general-purpose applications of AI in COVID-19 era, and (vi) AI role in drug design and repurposing (e.g., iteratively aligning protein spikes and applying three/four-fold symmetry to yield a low-resolution candidate template) against COVID-19. Further, we discuss the challenges involved in applying AI to the available data and privacy issues that can arise from personal data transitioning into cyberspace. We also provide a concise overview of other latest technologies that were increasingly applied to limit the spread of the ongoing pandemic. Finally, we discuss the avenues of future research in the respective area. This insightful review aims to highlight existing AI-based technological developments and future research dynamics in this area. [ABSTRACT FROM AUTHOR]

Abstract: Attribute-based encryption (ABE) is a promising cryptographic primitive for fine-grained access control of distributed data. In ciphertext policy attribute-based encryption (CP-ABE), each user is associated with a set of attributes and data are encrypted with access policies on attributes. A user is able to decrypt a ciphertext if and only if his attributes satisfy the access policy embedded in the ciphertext. However, key escrow is inherent in ABE systems. A curious key generation center in that construction has the power to decrypt every ciphertext. We found that most of the existing ABE schemes depending on a single key authority suffer from the key escrow problem. In this study, we propose a novel CP-ABE key issuing architecture that solves the key escrow problem. The proposed scheme separates the power of issuing user keys into two parties: the key generation center and the attribute authority. In the proposed construction, the key generation center and the attribute authority issue different parts of secret key components to users through a secure two-party computation protocol such that none of them can determine the whole set of keys of users individually. We demonstrate how the proposed key issuing protocol can be applied in the existing CP-ABE scheme and resolve the key escrow problem. [Copyright &y& Elsevier]

Abstract: Broadcast encryption enables a broadcaster to encrypt messages and transmit them to some subset S of authorized users. In identity-based broadcast encryption schemes, a broadcasting sender typically encrypts a message by combining public identities of receivers in S and system parameters. However, previous identity-based broadcast encryption schemes have not been concerned about preserving the privacy of receivers. Consequently, all of the identities of broadcast receivers in S are exposed to the public in the previous schemes, which may be subject to attacks on user privacy in lots of pragmatic applications. We propose a novel privacy-preserving identity-based broadcast encryption scheme against an active attacker. The proposed scheme protects the privacy of receivers of broadcasted messages by hiding the identities of receivers in S. Additionally, it achieves less storage and computation costs required to encrypt and decrypt the broadcast message, compared to the previous identity-based broadcast encryption schemes that do not provide user privacy. [Copyright &y& Elsevier]