Location Privacy Leakage through Sensory Data

Liang, Yi; Cai, Zhipeng; Han, Qilong; Li, Yingshu

doi:10.1155/2017/7576307

Cited by 43 publications

(20 citation statements)

References 29 publications

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“…For example, whether two individuals in a social network have a close relationship may be expected to be kept a secret. Therefore, privacy concerns have been raised in increasingly emerging technologies [2][3][4][5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

An Edge Correlation Based Differentially Private Network Data Release Method

Cai

Wang

et al. 2017

Security and Communication Networks

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Differential privacy (DP) provides a rigorous and provable privacy guarantee and assumes adversaries' arbitrary background knowledge, which makes it distinct from prior work in privacy preserving. However, DP cannot achieve claimed privacy guarantees over datasets with correlated tuples. Aiming to protect whether two individuals have a close relationship in a correlated dataset corresponding to a weighted network, we propose a differentially private network data release method, based on edge correlation, to gain the tradeoff between privacy and utility. Specifically, we first extracted the Edge Profile (PF) of an edge from a graph, which is transformed from a raw correlated dataset. Then, edge correlation is defined based on the PFs of both edges via JensonShannon Divergence (JS-Divergence). Secondly, we transform a raw weighted dataset into an indicated dataset by adopting a weight threshold, to satisfy specific real need and decrease query sensitivity. Furthermore, we propose -correlated edge differential privacy (CEDP), by combining the correlation analysis and the correlated parameter with traditional DP. Finally, we propose network data release (NDR) algorithm based on the -CEDP model and discuss its privacy and utility. Extensive experiments over real and synthetic network datasets show the proposed releasing method provides better utilities while maintaining privacy guarantee.

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Section: Introductionmentioning

confidence: 99%

An Edge Correlation Based Differentially Private Network Data Release Method

Cai

Wang

et al. 2017

Security and Communication Networks

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“…Data privacy mainly focuses on the content of the data submitted by DPs. Its importance lies in that data collected through MCS is usually sensitive to DPs, based on which it is easy for an entity to infer private information, such as the location [47], trajectory, etc. But at the same time, usability should be preserved.…”

Section: ) Data Privacymentioning

confidence: 99%

Privacy protection in mobile crowd sensing: a survey

et al. 2019

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The unprecedented proliferation of mobile smart devices has propelled a promising computing paradigm, Mobile Crowd Sensing (MCS), where people share surrounding insight or personal data with others. As a fast, easy, and cost-effective way to address large-scale societal problems, MCS is widely applied into many fields, e.g., environment monitoring, map construction, public safety, etc. Despite the popularity, the risk of sensitive information disclosure in MCS poses a serious threat to the participants and limits its further development in privacy-sensitive fields. Thus, the research on privacy protection in MCS becomes important and urgent. This paper targets the privacy issues of MCS and conducts a comprehensive literature research on it by providing a thorough survey. We first introduce a typical system structure of MCS, summarize its characteristics, propose essential requirements on privacy on the basis of a threat model. Then, we survey existing solutions on privacy protection and evaluate their performances by employing the proposed requirements. In essence, we classify the privacy protection schemes into four categories with regard to identity privacy, data privacy, attribute privacy, and task privacy. Besides, we review the achievements on privacy-preserving incentives in MCS from four viewpoints of incentive measures: credit incentive, auction incentive, currency incentive, and reputation incentive. Finally, we point out some open issues and propose future research directions based on the findings from our survey.

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“…Although this technology provides great benefits, sensitive and private information mined from raw data (e.g., social relationships and financial transactions) is also exposed to the risk of disclosure [6]. For example, more than 400, 000 electronic eyes in Beijing may lead to privacy leakage (e.g., vehicle location information) by data sharing in vehicular ad hoc networks (VANETs) [18,30,32]. Similarly, we can also illegal access personal health datasets gathered from various sensors of physical sign in body sensor networks (BSNs) and publish these private data without permission [34].…”

Section: (Communicated By Zhipeng Cai)mentioning

confidence: 99%

A real-time aggregate data publishing scheme with adaptive <i>ω</i>-event differential privacy

Yong¹,

Huo²,

Hu³

et al. 2018

Mathematical Foundations of Computing

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Although massive real-time data collected from users can provide benefits to improve the quality of human daily lives, it is possible to expose users' privacy. -differential privacy is a notable model to provide strong privacy preserving in statistics. The existing works highlight ω-event differential privacy with a fixed window size, which may not be suitable for many practical scenarios. In view of this issue, we explore a real-time scheme with adaptive ω-event for differentially private time-series publishing (ADP) in this paper. In specific, we define a novel notion, Quality of Privacy (QoP) to measure both the utility of the released statistics and the performance of privacy preserving. According to this, we present an adaptive ω-event differential privacy model that can provide privacy protection with higher accuracy and better privacy protection effect. In addition, we also design a smart grouping mechanism to improve the grouping performance, and then improve the availability of publishing statistics. Finally, comparing with the existing schemes, we exploit real-world and synthetic datasets to conduct several experiments to demonstrate the superior performance of the ADP scheme.1. Introduction. Recently, context-aware smart devices are experiencing an unprecedented surge in smart cyber-physical networks [39]. These devices can achieve ubiquitous connectivity and powerful raw data-gathering capabilities via humancentric and device-to-device (D2D)-based applications [31]. Using data mining technologies, we then can discover knowledge from huge amounts of raw data. Accordingly, we learn that the technology may provides great benefits to push against our cognition of human behavior and to improve the quality of our lives.

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Location Privacy Leakage through Sensory Data

Cited by 43 publications

References 29 publications

An Edge Correlation Based Differentially Private Network Data Release Method

An Edge Correlation Based Differentially Private Network Data Release Method

Privacy protection in mobile crowd sensing: a survey

A real-time aggregate data publishing scheme with adaptive <i>ω</i>-event differential privacy

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