A Survey of Trajectory Privacy-Preserving Techniques

Zheng, Haibin; Meng, Xiao-Feng

doi:10.3724/sp.j.1016.2011.01820

Cited by 30 publications

(21 citation statements)

References 2 publications

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“…Huo and Meng [33] classified trajectory privacy techniques into three categories, i.e., dummy location-based methods [65], generalized trajectory k-anonymity based methods, and suppression methods [64].…”

Section: ) Trajectory Privacy Preservation (Tr)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: ) Trajectory Privacy Preservation (Tr)mentioning

confidence: 99%

Privacy protection in mobile crowd sensing: a survey

et al. 2019

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“…The existing location data privacy protection methods [4,32] are mainly classified to three categories: the heuristic privacy-measure methods, the probability-based privacy inference methods and the privacy information retrieval methods. The heuristic privacy-measure methods are mainly to provide the privacy protection measure for some no-high required users, such as k-anonymity [19], t-closing [3], m-invariability [27] and l-diversity [25]. The information retrieval privacy protection methods may result in no data can be released, and these methods have high overhead.…”

Section: Related Workmentioning

confidence: 99%

Location Data Record Privacy Protection based on Differential Privacy Mechanism

Gu¹,

Yang²,

Yin³

2018

ITC

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Now many location data applications have facilitated people's daily life. However, publishing location data may divulge individual sensitive information. Currently many existing privacy protection schemes cannot provide the balance of utility and protection. Furthermore, as the records about location data may be discrete in database, some existing privacy protection schemes are difficult to protect location data information in data mining. In this paper, our works mainly focus on providing a framework for the privacy protection of location data mining. We propose a location data record privacy protection scheme based on differential privacy mechanism, which employs the structure of multi-level query tree to query and publish location data on database. Our proposed location data privacy protection scheme may discover the relationship of location data from database and protect location data mined. As accessing location preference of user may be related to private (sensitive) location, it is very important to protect highly frequent accessing location data when location data are mined. So, our proposed scheme provides a mechanism to protect highly frequent accessing location data (or accessing location preference of user) by distorting accessing frequencies. In the proposed scheme, we first construct the structure of multi-level query tree from database, then we make double processes of selecting data according to accessing frequencies by the exponential mechanism and one process of adding noises to accessing frequencies by the Laplace's mechanism on the multi-level query tree. Compared with the other schemes, the experiments show the data availability of the proposed scheme is higher and the privacy protection of the scheme is effective.

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“…Although their scheme can ensure data security, data utility is decreased. Current location data privacy protection methods [1,24] are mainly classified to three categories: the heuristic privacy-measure methods, the probability-based privacy inference methods and the privacy information retrieval's methods. The heuristic privacy-measure methods [25,26,27,28] are mainly to provide the privacy protection measure for some no-high required users, such as k-anonymity [25], t-closing [26], m-invariability [27] and l-diversity [28].…”

Section: Related Workmentioning

confidence: 99%

Trajectory Data Privacy Protection based on Differential Privacy Mechanism

Gu¹,

Yang²,

Liu³

et al. 2018

IJCAI

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Now many applications of trajectory (location) data have facilitated people's daily life. However, publishing trajectory data may divulge individual sensitive information so as to influence people's normal life. On the other hand, if we cannot mine and share trajectory data information, trajectory data will lose its value to serve our society. Currently, because the records of trajectory data are discrete in database, some existing privacy protection schemes are difficult to protect trajectory data. In this paper, we propose a trajectory data privacy protection scheme based on Laplace's differential privacy mechanism. In the proposed scheme, the algorithm first selects the protected points from the user's trajectory data; secondly, the algorithm builds the polygons according to the protected points and the adjacent and high frequent accessed points selected from the accessed point database, then the algorithm calculates the polygon centroids; finally, the noises are added to the polygon centroids by the Laplace's differential privacy method, and the new polygon centroids are used to replace the protected points, and then the algorithm constructs and issues the new trajectory data. The experiments show that the running time of the proposed algorithms is fast, the privacy protection of the scheme is effective and the data usability of the scheme is higher. Povzetek: Predlagana je metoda za učinkovito varovanje podatkov o poteh na osnovi Laplacove diferenčne privatnosti.

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A Survey of Trajectory Privacy-Preserving Techniques

Cited by 30 publications

References 2 publications

Privacy protection in mobile crowd sensing: a survey

Privacy protection in mobile crowd sensing: a survey

Location Data Record Privacy Protection based on Differential Privacy Mechanism

Trajectory Data Privacy Protection based on Differential Privacy Mechanism

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