Search Results for author:
Found 7 papers, 0 papers with code
Privacy and Bias Analysis of Disclosure Avoidance Systems
The results show that, contrary to popular beliefs, traditional differential privacy techniques may be superior in terms of accuracy and fairness to differential private counterparts of widely used DA mechanisms.
Fairness Increases Adversarial Vulnerability
The remarkable performance of deep learning models and their applications in consequential domains (e. g., facial recognition) introduces important challenges at the intersection of equity and security.
SF-PATE: Scalable, Fair, and Private Aggregation of Teacher Ensembles
A key characteristic of the proposed model is to enable the adoption of off-the-selves and non-private fair models to create a privacy-preserving and fair model.
Differential Privacy and Fairness in Decisions and Learning Tasks: A Survey
This paper surveys recent work in the intersection of differential privacy (DP) and fairness.
Post-processing of Differentially Private Data: A Fairness Perspective
Post-processing immunity is a fundamental property of differential privacy: it enables arbitrary data-independent transformations to differentially private outputs without affecting their privacy guarantees.
Bias and Variance of Post-processing in Differential Privacy
Moreover, when the feasible region is convex, a widely adopted class of post-processing steps is also guaranteed to improve accuracy.
Differential Privacy of Hierarchical Census Data: An Optimization Approach
To address them, this paper presents a novel differential-privacy mechanism for releasing hierarchical counts of individuals.
