PGLP: Customizable and Rigorous Location Privacy Through Policy Graph

Cao, Yang; Xiao, Yonghui; Takagi, Shun; Xiong, Li; Yoshikawa, Masatoshi; Shen, Yilin; Liu, Jinfei; Jin, Hongxia; Xu, Xiaofeng

doi:10.1007/978-3-030-58951-6_32

Cited by 5 publications

(4 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Cao et al [39] proposed the generalized version of GeoI using a policy graph to enable us to customize the privacy of GeoI. They demonstrate the flexibility of privacy to achieve better utility in the COVID-19 case [40].…”

Section: State-of-the-art Privacy Modelsmentioning

confidence: 99%

Geo-Graph-Indistinguishability: Location Privacy on Road Networks with Differential Privacy

Takagi

Cao

Asano

et al. 2023

IEICE Trans. Inf. & Syst.

Self Cite

View full text Add to dashboard Cite

In recent years, concerns about location privacy are increasing with the spread of location-based services (LBSs). Many methods to protect location privacy have been proposed in the past decades. Especially, perturbation methods based on Geo-Indistinguishability (GeoI), which randomly perturb a true location to a pseudolocation, are getting attention due to its strong privacy guarantee inherited from differential privacy. However, GeoI is based on the Euclidean plane even though many LBSs are based on road networks (e.g. ride-sharing services). This causes unnecessary noise and thus an insufficient tradeoff between utility and privacy for LBSs on road networks. To address this issue, we propose a new privacy notion, Geo-Graph-Indistinguishability (GeoGI), for locations on a road network to achieve a better tradeoff. We propose Graph-Exponential Mechanism (GEM), which satisfies GeoGI. Moreover, we formalize the optimization problem to find the optimal GEM in terms of the tradeoff. However, the computational complexity of a naive method to find the optimal solution is prohibitive, so we propose a greedy algorithm to find an approximate solution in an acceptable amount of time. Finally, our experiments show that our proposed mechanism outperforms GeoI mechanisms, including optimal GeoI mechanism, with respect to the tradeoff.

show abstract

Section: State-of-the-art Privacy Modelsmentioning

confidence: 99%

Geo-Graph-Indistinguishability: Location Privacy on Road Networks with Differential Privacy

Takagi

Cao

Asano

et al. 2023

IEICE Trans. Inf. & Syst.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Our model requires only infected people to participate, so such a problem does not occur. As this work proposes, several papers proposed relaxation of DP by defining a policy to improve utility [5,20,22]. Their policy is based on a discriminative pair, so the asymmetricity that we utilize does not occur.…”

Section: Related Workmentioning

confidence: 99%

“…Since 𝑞(𝐷 𝑘 ) = 1, Pr[𝑚(𝐷 𝑘 ) ∈ 𝑆] is the probability that we achieve OTP. Therefore, by reformulating (5),…”

Section: Theorem 9 (Otp In (𝑃 𝜖)-Adp)mentioning

confidence: 99%

See 1 more Smart Citation

Asymmetric Differential Privacy

Takagi¹,

Cao²,

Yoshikawa³

2021

Preprint

Self Cite

View full text Add to dashboard Cite

Recently, differential privacy (DP) is getting attention as a privacy definition when publishing statistics of a dataset. However, when answering a decision problem with a DP mechanism, it causes a two-sided error. This characteristic of DP is not desirable when publishing risk information such as concerning COVID-19. This paper proposes relaxing DP to mitigate the limitation and improve the utility of published information. First, we define a policy that separates information into sensitive and non-sensitive. Then, we define asymmetric differential privacy (ADP) that provides the same privacy guarantee as DP to sensitive information. This partial protection induces asymmetricity in privacy protection to improve utility and allow a one-sided error mechanism. Following ADP, we propose two mechanisms for two tasks based on counting query with utilizing these characteristics: top-𝑘 query and publishing risk information of viruses with an accuracy guarantee. Finally, we conducted experiments to evaluate proposed algorithms using real-world datasets and show their practicality and improvement of the utility, comparing state-of-the-art algorithms.

show abstract

Vector-Indistinguishability: Location Dependency Based Privacy Protection for Successive Location Data

Zhao

Chen

2024

IEEE Trans. Comput.

View full text Add to dashboard Cite

PGLP: Customizable and Rigorous Location Privacy Through Policy Graph

Cited by 5 publications

References 36 publications

Geo-Graph-Indistinguishability: Location Privacy on Road Networks with Differential Privacy

Geo-Graph-Indistinguishability: Location Privacy on Road Networks with Differential Privacy

Asymmetric Differential Privacy

Vector-Indistinguishability: Location Dependency Based Privacy Protection for Successive Location Data

Contact Info

Product

Resources

About