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20202026
most citedNetwork Shuffling: Privacy Amplification via Random Walks

11 citations · 31 across the 14 of their papers we have counts for

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5 papers · 1 filter

cs.CR2020★ 2 cited

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

Shun Takagi, Yang Cao, Yasuhito Asano +1

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 th…

cs.LG2020★ 3 cited

Differentially Private Variational Autoencoders with Term-wise Gradient Aggregation

Tsubasa Takahashi, Shun Takagi, Hajime Ono +1

This paper studies how to learn variational autoencoders with a variety of divergences under differential privacy constraints. We often build a VAE with an appropriate prior distri…

cs.DB2020★ 1 cited

PANDA: Policy-aware Location Privacy for Epidemic Surveillance

Yang Cao, Shun Takagi, Yonghui Xiao +2

In this demonstration, we present a privacy-preserving epidemic surveillance system. Recently, many countries that suffer from coronavirus crises attempt to access citizen's locati…

cs.LG2020★ 4 cited

P3GM: Private High-Dimensional Data Release via Privacy Preserving Phased Generative Model

Shun Takagi, Tsubasa Takahashi, Yang Cao +1

How can we release a massive volume of sensitive data while mitigating privacy risks? Privacy-preserving data synthesis enables the data holder to outsource analytical tasks to an…

cs.CR2020★ 3 cited

PGLP: Customizable and Rigorous Location Privacy through Policy Graph

Yang Cao, Yonghui Xiao, Shun Takagi +6

Location privacy has been extensively studied in the literature. However, existing location privacy models are either not rigorous or not customizable, which limits the trade-off b…