UTM: A trajectory privacy evaluating model for online health monitoring

Yang, Zhigang; Wang, Ruyan; Wang, Dapeng; Luo, Daizhong

doi:10.1016/j.dcan.2020.10.001

Cited by 9 publications

(2 citation statements)

References 25 publications

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“…Therefore, we plan to study how to customize the differential privacy budgets and noise generation methods according to the predicted re-identification risk for specific individuals, and how to maximize the preservation of the attribute dependence and value dependence information. In addition, trajectory datasets typically contain temporal and sequential location data with strong attribute dependence 35 , which makes the binomial approximation ineffective in privacy risk prediction. This also points out a new research direction for future work.…”

Section: Discussionmentioning

confidence: 99%

Re-identification risk prediction paradigm using incomplete statistical information and recursive hypergeometric distribution

Yang

Wang

et al. 2021

Preprint

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The dataset anonymization has not eliminated the re-identification risk, the evaluation of which remains a huge challenge, especially given incomplete statistical information. The re-identification risk of individuals depends on their tuple frequency. The paper proposes the recursive hypergeometric (RH) distribution to accurately calculate the tuple frequency and leverages the binomial distribution to approximate the RH distribution and to efficiently predict the re-identification risk of individuals in both generated and real-world datasets. The experimental results show that our tuple frequency based re-identification risk (TFRR) prediction model has a superior performance (average AUC 0.86~0.98) for all types of datasets. Furthermore, we exploit the value dependence knowledge to rectify the prediction result for some subsets (average AUC 0.95~0.98). Our research reveals the general rule of the tuple frequency distribution and enables individuals and regulators to responsively predict the re-identification risk.

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Section: Discussionmentioning

confidence: 99%

Re-identification risk prediction paradigm using incomplete statistical information and recursive hypergeometric distribution

Yang

Wang

et al. 2021

Preprint

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“…Firstly, the sensing data collected by MCS often involves the user's location data that contains abundant personal information. If an attacker obtains the user's geographic location from the perceived data, the user's activity range can be inferred [15,16]. To protect sensitive information of mobile users, most studies encrypt or add noise to the sensing data, such as local differential privacy [17,18].…”

Section: Introductionmentioning

confidence: 99%

Private Data Aggregation Based on Fog-Assisted Authentication for Mobile Crowd Sensing

Wang

Zhang

Yang

et al. 2021

Security and Communication Networks

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In mobile crowd sensing (MCS), the cloud as a single sensing platform undertakes a large number of communication tasks, leading to the reduction of sensing task execution efficiency and the risk of loss and leakage of users’ private data. In this paper, we propose a spatial ciphertext aggregation scheme with collaborative verification of fog nodes. Firstly, the cloud and fog collaboration architecture is constructed. Fog nodes are introduced for data validation and slices transmission, reducing computing cost on the sensing platform. Secondly, a multipath transmission method of slice data is proposed, in which the user identity and data are transmitted anonymously by the secret sharing method, and the data integrity is guaranteed by hash chain authentication. Finally, a spatial data aggregation method based on privacy protection is presented. The ciphertext aggregation calculation of the sensing platform is realized through Paillier homomorphic encryption, and the problem of insufficient data coverage in the sensing region is solved by the position-based weight interpolation method. The security analysis demonstrates that the scheme can achieve the expected security goal. The simulation results show the feasibility and effectiveness of the proposed scheme.

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