An efficient privacy preserving data aggregation approach for mobile sensing

Zhang, Lichen; Wang, Xiaoming; Lu, Junling; Li, Peng; Cai, Zhipeng

doi:10.1002/sec.1546

Cited by 23 publications

(13 citation statements)

References 33 publications

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“…Similarly, Zhang et al employed Paillier homomorphism encryption for data privacy preservation and designed a data summation protocol [81]. Utilization of homomorphism encryption protects data privacy and supports data accountability.…”

Section: Data Privacy Preservation (Da)mentioning

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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Section: Data Privacy Preservation (Da)mentioning

confidence: 99%

Privacy protection in mobile crowd sensing: a survey

et al. 2019

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“…Attackers not only want to infer privacy information, but also try to do it efficiently [39]. In order to save energy, Yadav et al [21] proposed their low cost GSM-based localization method based on Cell Broadcast Messages and war-driving.…”

Section: Related Workmentioning

confidence: 99%

Location Privacy Leakage through Sensory Data

Liang

Cai

Han

et al. 2017

Security and Communication Networks

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Mobile devices bring benefits as well as the risk of exposing users' location information, as some embedded sensors can be accessed without users' permission and awareness. In this paper, we show that, only by using the data collected from the embedded sensors in mobile devices instead of GPS data, we can infer a user's location information with high accuracy. Three issues are addressed which are route identification, user localization in a specific route, and user localization in a bounded area. The Dynamic Time Warping based technique is designed and we develop a Hidden Markov Model to solve the localization problem. Real experiments are performed to evaluate our proposed methods.

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“…Meanwhile, individual privacy issues on smartphones are increasingly receiving attentions due to the risk of disclosure of user's privacy sensitive information. Various approaches have been proposed to protect users' sensitive information in location-based services (LBSs) and participatory sensing applications [11]. In fact, most previous privacy protection techniques focus on the static scenarios [12][13][14][15][16][17][18][19], in which the instant sensitive location information is protected without consideration of temporal correlations among locations.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Context-Aware Privacy Preserving Approach for Smartphones

Zhang

Wang

et al. 2017

Security and Communication Networks

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With the proliferation of smartphones and the usage of the smartphone apps, privacy preservation has become an important issue. The existing privacy preservation approaches for smartphones usually have less efficiency due to the absent consideration of the active defense policies and temporal correlations between contexts related to users. In this paper, through modeling the temporal correlations among contexts, we formalize the privacy preservation problem to an optimization problem and prove its correctness and the optimality through theoretical analysis. To further speed up the running time, we transform the original optimization problem to an approximate optimal problem, a linear programming problem. By resolving the linear programming problem, an efficient context-aware privacy preserving algorithm (CAPP) is designed, which adopts active defense policy and decides how to release the current context of a user to maximize the level of quality of service (QoS) of context-aware apps with privacy preservation. The conducted extensive simulations on real dataset demonstrate the improved performance of CAPP over other traditional approaches.

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An efficient privacy preserving data aggregation approach for mobile sensing

Cited by 23 publications

References 33 publications

Privacy protection in mobile crowd sensing: a survey

Privacy protection in mobile crowd sensing: a survey

Location Privacy Leakage through Sensory Data

An Efficient Context-Aware Privacy Preserving Approach for Smartphones

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