Asymmetric Differential Privacy

Abstract

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.

DeVoS: Deniable Yet Verifiable Vote Updating

“…Indeed, in DeVoS, the PT can only add dummy votes (and hide the presence of a ballot), but cannot remove valid ballots (to hide the absence of a ballot), as this would directly contradict the correctness of the final result. This asymmetry could be built directly into the definition, as in [37,51]. In this paper we use one-sided DP (OSDP) [37], which only protects the sensitive state defined by policy 𝑃.…”