activity
20092023
most citedMembership Inference Attacks and Defenses in Classification Models

63 citations · 279 across the 28 of their papers we have counts for

collaborators
Showing 2020Show all

7 papers · 1 filter

cs.CY2020★ 1 cited

PURE: A Framework for Analyzing Proximity-based Contact Tracing Protocols

Fabrizio Cicala, Weicheng Wang, Tianhao Wang +4

Many proximity-based tracing (PCT) protocols have been proposed and deployed to combat the spreading of COVID-19. In this paper, we take a systematic approach to analyze PCT protoc…

cs.CR2020★ 7 cited

Black-box Model Inversion Attribute Inference Attacks on Classification Models

Shagufta Mehnaz, Ninghui Li, Elisa Bertino

Increasing use of ML technologies in privacy-sensitive domains such as medical diagnoses, lifestyle predictions, and business decisions highlights the need to better understand if…

cs.CR2020★ 1 cited

Answering Multi-Dimensional Range Queries under Local Differential Privacy

Jianyu Yang, Tianhao Wang, Ninghui Li +2

In this paper, we tackle the problem of answering multi-dimensional range queries under local differential privacy. There are three key technical challenges: capturing the correlat…

cs.CR2020★ 1 cited

PolyScope: Multi-Policy Access Control Analysis to Triage Android Systems

Yu-Tsung Lee, William Enck, Haining Chen +6

Android filesystem access control provides a foundation for Android system integrity. Android utilizes a combination of mandatory (e.g., SEAndroid) and discretionary (e.g., UNIX pe…

cs.CR2020★ 3 cited

Continuous Release of Data Streams under both Centralized and Local Differential Privacy

Tianhao Wang, Joann Qiongna Chen, Zhikun Zhang +5

In this paper, we study the problem of publishing a stream of real-valued data satisfying differential privacy (DP). One major challenge is that the maximal possible value can be q…

cs.CR2020★ 3 cited

Towards Effective Differential Privacy Communication for Users' Data Sharing Decision and Comprehension

Aiping Xiong, Tianhao Wang, Ninghui Li +1

Differential privacy protects an individual's privacy by perturbing data on an aggregated level (DP) or individual level (LDP). We report four online human-subject experiments inve…