PriSTE: From Location Privacy to Spatiotemporal Event Privacy

Cao, Yang; Xiao, Yonghui; Xiong, Li; Bai, Liquan

doi:10.1109/icde.2019.00153

Cited by 42 publications

(24 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…given observations o 1 , o 2 , · · · , o T and a given user's initial probability π, so that we can directly calculate the Pr(o 1 , o 2 , · · · , o T |EVENT). In Section 4, we will design a mechanism for spatiotemporal event 1. If the Markov model is high-ordered, i.e., the transition matrix has a larger state domain, our approach still works.…”

Section: Overview Of Our Approachmentioning

confidence: 99%

“…A natural question is whether we can always find an α to release a perturbed location that satisfies Equation (1). The answer is affirmative because α converges exponentially to 0.…”

Section: Case Study 1: Priste With Geo-indistinguishabilitymentioning

confidence: 99%

“…Thus start = 2, end = 5. The regions are s2 = [1, 1, 0] , s3 = [0, 1, 1] , s4 = [1, 1, 0] , s5 = [0, 1,1] . To derive the probability of one trajectory, e.g.…”

Section: Appendix B Naive Solutions B1 Computing Prior Probability Omentioning

confidence: 99%

See 2 more Smart Citations

Protecting Spatiotemporal Event Privacy in Continuous Location-Based Services

Cao

Xiao

Xiong

et al. 2021

IEEE Trans. Knowl. Data Eng.

Self Cite

View full text Add to dashboard Cite

Location privacy-preserving mechanisms (LPPMs) have been extensively studied for protecting users' location privacy by releasing a perturbed location to third parties such as location-based service providers. However, when a user's perturbed locations are released continuously, existing LPPMs may not protect the sensitive information about the user's spatiotemporal activities, such as "visited hospital in the last week" or "regularly commuting between Address 1 and Address 2" (it is easy to infer that Addresses 1 and 2 may be home and office), which we call it spatiotemporal event. In this paper, we first formally define spatiotemporal event as Boolean expressions between location and time predicates, and then we define -spatiotemporal event privacy by extending the notion of differential privacy. Second, to understand how much spatiotemporal event privacy that existing LPPMs can provide, we design computationally efficient algorithms to quantify the privacy leakage of state-of-the-art LPPMs when an adversary has prior knowledge of the user's initial probability over possible locations. It turns out that the existing LPPMs cannot adequately protect spatiotemporal event privacy. Third, we propose a framework, PriSTE, to transform an existing LPPM into one protecting spatiotemporal event privacy against adversaries with any prior knowledge. Our experiments on real-life and synthetic data verified that the proposed method is effective and efficient.

show abstract

Section: Overview Of Our Approachmentioning

confidence: 99%

“…A natural question is whether we can always find an α to release a perturbed location that satisfies Equation (1). The answer is affirmative because α converges exponentially to 0.…”

Section: Case Study 1: Priste With Geo-indistinguishabilitymentioning

confidence: 99%

See 1 more Smart Citation

Protecting Spatiotemporal Event Privacy in Continuous Location-Based Services

Cao

Xiao

Xiong

et al. 2021

IEEE Trans. Knowl. Data Eng.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Cao et al [9] [11] [12] proposed a method to define the privacy goal, which consisted of spatial and temporal goals. However, even in these studies, it was not mentioned about how to define information such as the place and time.…”

Section: Location Privacy Preference Definitionmentioning

confidence: 99%

“When and Where Do You Want to Hide?” – Recommendation of Location Privacy Preferences with Local Differential Privacy

Asada

Yoshikawa

Cao

2019

Data and Applications Security and Privacy XXXIII

Self Cite

View full text Add to dashboard Cite

In recent years, it has become easy to obtain location information quite precisely. However, the acquisition of such information has risks such as individual identification and leakage of sensitive information, so it is necessary to protect the privacy of location information. For this purpose, people should know their location privacy preferences, that is, whether or not he/she can release location information at each place and time. However, it is not easy for each user to make such decisions and it is troublesome to set the privacy preference at each time. Therefore, we propose a method to recommend location privacy preferences for decision making. Comparing to existing method, our method can improve the accuracy of recommendation by using matrix factorization and preserve privacy strictly by local differential privacy, whereas the existing method does not achieve formal privacy guarantee. In addition, we found the best granularity of a location privacy preference, that is, how to express the information in location privacy protection. To evaluate and verify the utility of our method, we have integrated two existing datasets to create a rich information in term of user number. From the results of the evaluation using this dataset, we confirmed that our method can predict location privacy preferences accurately and that it provides a suitable method to define the location privacy preference.

show abstract

“…But the recent studies revealed that k-anonymity might not be rigorous enough since they suffer many realistic attacks [14,16] when the adversary has background knowledge about the original dataset. The recent state-of-the-art location privacy models [3,22,21,20,23,5,6,7] were extended from differential privacy (DP) [10] to private location release since DP is considered a rigorous privacy notion. Although these DP-based location privacy models are rigorously defined, yet they are not flexible and customizable for different scenarios with various requirements on privacy-utility trade-off.…”

Section: Introductionmentioning

confidence: 99%

PriSTE

Cao¹,

Xiao

Xiong

et al. 2019

Proc. VLDB Endow.

Self Cite

View full text Add to dashboard Cite

In this demonstration, we present a privacy-preserving epidemic surveillance system. Recently, many countries that suffer from coronavirus crises attempt to access citizen's location data to eliminate the outbreak. However, it raises privacy concerns and may open the doors to more invasive forms of surveillance in the name of public health. It also brings a challenge for privacy protection techniques: how can we leverage people's mobile data to help combat the pandemic without scarifying our location privacy. We demonstrate that we can have the best of the two worlds by implementing policy-based location privacy for epidemic surveillance. Specifically, we formalize the privacy policy using graphs in light of differential privacy, called policy graph. Our system has three primary functions for epidemic surveillance: location monitoring, epidemic analysis, and contact tracing. We provide an interactive tool allowing the attendees to explore and examine the usability of our system: (1) the utility of location monitor and disease transmission model estimation, (2) the procedure of contact tracing in our systems, and (3) the privacy-utility trade-offs w.r.t. different policy graphs. The attendees can find that it is possible to have the full functionality of epidemic surveillance while preserving location privacy. PVLDB Reference Format:

show abstract

PriSTE: From Location Privacy to Spatiotemporal Event Privacy

Cited by 42 publications

References 24 publications

Protecting Spatiotemporal Event Privacy in Continuous Location-Based Services

Protecting Spatiotemporal Event Privacy in Continuous Location-Based Services

“When and Where Do You Want to Hide?” – Recommendation of Location Privacy Preferences with Local Differential Privacy

PriSTE

Contact Info

Product

Resources

About