Privacy preservation using game theory in e-health application

Sfar, Arbia Riahi; Natalizio, Enrico; Mazlout, Sahbi; Challal, Yacine; Chtourou, Zied

doi:10.1016/j.jisa.2022.103158

Cited by 3 publications

(4 citation statements)

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“…Akkaoui et al [26] studied a blockchain-based sharing framework, constructed a patient-requester game model from an EGT perspective, investigated the impact of relevant parameters in medical big data sharing in terms of trust, and validated the results through numerical simulations. In the e-health application scenario, Sfar et al [27] proposed a Markov process-based privacy-preserving game model (MTGM), which can not only enhance privacy protection and improve time efficiency but also resolve the conflict of interest between data holders and data requesters to a certain extent. To balance the accuracy of medical diagnosis and the security of patient privacy, Jiang et al [28] proposed a multi-party evolutionary game model based on UPHFPR to quantify the risk of physician visits.…”

Section: Game Theory On Data Sharingmentioning

confidence: 99%

An Evolutionary Game Analysis of Stakeholders’ Decision-Making Behavior in Medical Data Sharing

2023

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In the era of big data, medical data sharing has become an inevitable requirement to improve the quality and efficiency of medical services. To advance the progress of medical data sharing and expedite the circulation of data value, it becomes crucial to examine the decision-making behavior of stakeholders involved in the medical-data-sharing process. To this end, we construct a three-way evolutionary game model applicable to the medical sharing scenario, analyzing the evolutionary trends in the selection strategies of data providers, the medical-data-sharing platform and data demanders. Furthermore, through theoretical analysis and simulation experiments, we explore the game equilibrium point of the system and analyze key factors that affect stakeholder strategy selection. The results of the experiment show that, in addition to data security, platforms and regulators should pay attention to the regulation and governance of the quality of data flows, which involves reasonable incentives–feedback–rewards and penalties. By strengthening the security technology and data governance system construction of sharing platforms, as well as promoting regulatory authorities to implement reward and punishment measures, etc., a stable state can be achieved in such systems. In addition, this article also proposes relevant management suggestions for medical data sharing in order to provide useful references for scientific decision making by stakeholders.

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Section: Game Theory On Data Sharingmentioning

confidence: 99%

An Evolutionary Game Analysis of Stakeholders’ Decision-Making Behavior in Medical Data Sharing

2023

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“…These approaches employ variety of operations such as generalization [14], suppression [15], bucketization [16], hash functions [17], cryptographic primitives [18], lattice-based encryption [19], parameter sharing [20], masking [21,22], pseudonyms [23][24][25][26], and joint operations [27][28][29][30] in order to preserve the privacy of the individual. Recently, machine learning (ML) approaches have also been employed to preserve the privacy of individuals in data analysis and publishing [31][32][33][34][35].…”

Section: State-of-the-art Privacy Preserving Approachesmentioning

confidence: 99%

“…Tadic et al [167] designed a prototype that preserves the privacy and security of activists online. Sfar et al [18] proposed generalized privacy-preserving solutions for e-health applications using the game theory concept. Zhang et al [168] discussed the concept of visual privacy which has become a major threat to the individual as well as group privacy amid rapid developments in AI tools.…”

Section: Group Privacy: a New Dimension Of Privacymentioning

confidence: 99%

“…Wu et al [146] Theoretical Suggests a method for self-identity protection across social networks × × Reviglio et al [147] Theoretical Highlights pertinent threats to group privacy in data mining × Gstrein et al [148] Theoretical Discusses many group privacy issues in datafication paradox × Mavriki et al [149] Theoretical Discusses implications of group privacy on general public × Mavriki et al [150] Technical Discusses group privacy issues in e-health applications with examples × Heinrichs et al [151] Theoretical Highlight group privacy issues caused by the AI tools × Mühlhoff et al [152] Theoretical Suggests a group privacy protection in predictive analytics × Mavriki et al [153] Theoretical Suggests protecting the interests of groups in big data era × Kikuchi et al [154] Theoretical Highlights the need of group privacy protection in purchase records × Flood et al [155] Theoretical Discusses group privacy issues in COVID-19 contact tracing apps × Alanezi et al [156] Technical Solves the group privacy problem in IoT scenarios using diversity concept Wickrama et al [157] Theoretical Discusses group privacy issues in smart homes environments × Kim et al [158] Theoretical Discusses AI effects on group privacy and their implications × Kim et al [159] Theoretical Highlights threats to group privacy in social networks analysis and mining × Russo et al [160] Technical Suggests a privacy protection method to access online social network services × Labs et al [161] Theoretical Highlights privacy issues in COVID-19 era (surveillance related) × Alshawi et al [162] Theoretical Describes group privacy-related issues in COVID-19 tracing apps × Sean et al [163] Theoretical Describes privacy concerns in group chat tools × Petrén et al [164] Technical Describes DP effect on group privacy (or decisions) preservation Erickson et al [165] Theoretical Describes many potential group privacy issues in Femtech app × Perino et al [166] Theoretical Highlights AI effect on users privacy and change in privacy landscape × Tadic et al [167] Technical Develops a practical tool for solving group privacy issues online Sfar et al [18] Technical Proposed a generalized privacy protection solution for e-health sector × Zhang et al [168] Theoretical Highlights the concept of visual privacy in deep learning systems...…”

Section: Threats To Group Privacy Discussedmentioning

confidence: 99%

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Group Privacy: An Underrated but Worth Studying Research Problem in the Era of Artificial Intelligence and Big Data

2022

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Introduction: Recently, the tendency of artificial intelligence (AI) and big data use/applications is 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.

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Privacy Prevention Techniques in Cyber Physical System for Pharmaceutical Care Services

Devliyal

Sharma

Goyal

2023

2023 International Conference on Power, Instrumentation, Control and Computing (PICC)

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Privacy preservation using game theory in e-health application

Cited by 3 publications

References 25 publications

An Evolutionary Game Analysis of Stakeholders’ Decision-Making Behavior in Medical Data Sharing

An Evolutionary Game Analysis of Stakeholders’ Decision-Making Behavior in Medical Data Sharing

Group Privacy: An Underrated but Worth Studying Research Problem in the Era of Artificial Intelligence and Big Data

Privacy Prevention Techniques in Cyber Physical System for Pharmaceutical Care Services

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