A Personalization-Privacy Paradox in Usage of Mobile Health Services: A Game Theoretic Perspective

As health information privacy concern of the public raises, people are hesitant on disclosure of their private health information for personalized health services from using mobile health. The tension between personalization and privacy hinders users’ adoption of mobile health services. In this study, we draw on game theory to explain the personalization-privacy paradox in the usage of mobile health services. The results show that: (1) In a one-shot game, the strategy set of mobile health marketers and users will be contrary to their original motivations. (2) In a repeated game, collecting users’ private health information in a friendly way and disclosing private health information will be dominant strategies for both players. Managers need to pay attention to these scenarios in promoting usage of mobile health services

On Collaborative Predictive Blacklisting

Collaborative predictive blacklisting (CPB) allows to forecast future attack sources based on logs and alerts contributed by multiple organizations. Unfortunately, however, research on CPB has only focused on increasing the number of predicted attacks but has not considered the impact on false positives and false negatives. Moreover, sharing alerts is often hindered by confidentiality, trust, and liability issues, which motivates the need for privacy-preserving approaches to the problem. In this paper, we present a measurement study of state-of-the-art CPB techniques, aiming to shed light on the actual impact of collaboration. To this end, we reproduce and measure two systems: a non privacy-friendly one that uses a trusted coordinating party with access to all alerts (Soldo et al., 2010) and a peer-to-peer one using privacy-preserving data sharing (Freudiger et al., 2015). We show that, while collaboration boosts the number of predicted attacks, it also yields high false positives, ultimately leading to poor accuracy. This motivates us to present a hybrid approach, using a semi-trusted central entity, aiming to increase utility from collaboration while, at the same time, limiting information disclosure and false positives. This leads to a better trade-off of true and false positive rates, while at the same time addressing privacy concerns.Comment: A preliminary version of this paper appears in ACM SIGCOMM's Computer Communication Review (Volume 48 Issue 5, October 2018). This is the full versio

PRIMA — Privacy research through the perspective of a multidisciplinary mash up

Based on a summary description of privacy protection research within three fields of inquiry, viz. social sciences, legal science, and computer and systems sciences, we discuss multidisciplinary approaches with regard to the difficulties and the risks that they entail as well as their possible advantages. The latter include the identification of relevant perspectives of privacy, increased expressiveness in the formulation of research goals, opportunities for improved research methods, and a boost in the utility of invested research efforts

cRVR: A Stackelberg Game Approach for Joint Privacy-Aware Video Requesting and Edge Caching

As users conveniently stream their favored online videos, video request records will be automatically seized by video content providers, which may leak users' privacy. Unfortunately, most existing privacy-enhancing approaches are not applicable for protecting users' privacy in requests, which cannot be easily altered or distorted by users and must be visible for content providers to stream correct videos. To preserve request privacy in online video services, it is possible to request additional videos irrelevant to users' interests so that content providers cannot precisely infer users' interest information. However, a naive redundant requesting approach will significantly degrade the performance of edge caches and increase bandwidth overhead accordingly. In this paper, we are among the first to propose a Cache-Friendly Redundant Video Requesting (cRVR) algorithm for User Devices (UDs) and its corresponding caching algorithm for the Edge Cache (EC), which can effectively mitigate the problem of request privacy leakage with minimal impact on the EC's performance. To solve the problem, we develop a Stackelberg game to analyze the dedicated interaction between UDs and EC and obtain their optimal strategies to maximize their respective utility. For UDs, the utility function is a combination of both video playback utility and privacy protection utility. We theoretically prove the existence and uniqueness of the equilibrium of the Stackelberg game. In the end, extensive experiments are conducted with real traces to demonstrate that cRVR can effectively protect video request privacy by reducing up to 57.96\% of privacy disclosure compared to baseline algorithms. Meanwhile, the caching performance of ECs is only slightly affected

The control over personal data: True remedy or fairy tale ?

This research report undertakes an interdisciplinary review of the concept of "control" (i.e. the idea that people should have greater "control" over their data), proposing an analysis of this con-cept in the field of law and computer science. Despite the omnipresence of the notion of control in the EU policy documents, scholarly literature and in the press, the very meaning of this concept remains surprisingly vague and under-studied in the face of contemporary socio-technical environments and practices. Beyond the current fashionable rhetoric of empowerment of the data subject, this report attempts to reorient the scholarly debates towards a more comprehensive and refined understanding of the concept of control by questioning its legal and technical implications on data subject\^as agency