Geo-indistinguishability: A Principled Approach to Location Privacy

Chatzikokolakis, Konstantinos; Palamidessi, Catuscia; Stronati, Marco

doi:10.1007/978-3-319-14977-6_4

Cited by 28 publications

(22 citation statements)

References 38 publications

(49 reference statements)

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“…This method ignores the relevance of user location points and is vulnerable to a large number of inference attacks. Chatzikokolakis et al [32] showed a formal notion of privacy that protects the user's exact location-"geoindistinguishability." In [32], they proposed two mechanisms to protect the privacy of user when dealing with location-based services.…”

Section: Related Workmentioning

confidence: 99%

Travel Trajectory Frequent Pattern Mining Based on Differential Privacy Protection

Wang

Yang

Bao

et al. 2021

Wireless Communications and Mobile Computing

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Now, many application services based on location data have brought a lot of convenience to people’s daily life. However, publishing location data may divulge individual sensitive information. Because the location records about location data may be discrete in the database, some existing privacy protection schemes are difficult to protect location data in data mining. In this paper, we propose a travel trajectory data record privacy protection scheme (TMDP) based on differential privacy mechanism, which employs the structure of a trajectory graph model on location database and frequent subgraph mining based on weighted graph. Time series is introduced into the location data; the weighted trajectory model is designed to obtain the travel trajectory graph database. We upgrade the mining of location data to the mining of frequent trajectory graphs, which can discover the relationship of location data from the database and protect location data mined. In particular, to improve the identification efficiency of frequent trajectory graphs, we design a weighted trajectory graph support calculation algorithm based on canonical code and subgraph structure. Moreover, to improve the data utility under the premise of protecting user privacy, we propose double processes of adding noises to the subgraph mining process by the Laplace mechanism and selecting final data by the exponential mechanism. Through formal privacy analysis, we prove that our TMDP framework satisfies ε -differential privacy. Compared with the other schemes, the experiments show that the data availability of the proposed scheme is higher and the privacy protection of the scheme is effective.

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Section: Related Workmentioning

confidence: 99%

Travel Trajectory Frequent Pattern Mining Based on Differential Privacy Protection

Wang

Yang

Bao

et al. 2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…In addition, their work tested the quality of the predicted location. Chatzikokolakis et al [7] also showed a formal notion of privacy that protects the user's exact location-"geo-indistinguishability". In [7], they proposed two mechanisms to protect the privacy of user when dealing with location-based services.…”

Section: Related Workmentioning

confidence: 99%

“…Chatzikokolakis et al [7] also showed a formal notion of privacy that protects the user's exact location-"geo-indistinguishability". In [7], they proposed two mechanisms to protect the privacy of user when dealing with location-based services. They extended their mechanisms to limit the degradation of the privacy guarantees due to the correlation between the points.…”

Section: Related Workmentioning

confidence: 99%

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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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“…The proposed system gets different speed trajectory by using a hierarchical reference method to isolate the original trajectory, and then protects the speed trajectory. Chatzikokolakis et al [32] proposed a predictive differentially-private mechanism for location privacy, which can offer substantial improvements over the independently applied noise. Their works showed that correlations in the trace can be in fact exploited in terms of a prediction function that tries to guess the new location based on the previously reported locations.…”

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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Geo-indistinguishability: A Principled Approach to Location Privacy

Cited by 28 publications

References 38 publications

Travel Trajectory Frequent Pattern Mining Based on Differential Privacy Protection

Travel Trajectory Frequent Pattern Mining Based on Differential Privacy Protection

Location Data Record Privacy Protection based on Differential Privacy Mechanism

Trajectory Data Privacy Protection based on Differential Privacy Mechanism

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