POSTER: Data Collection via Local Differential Privacy with Secret Parameters

Abstract

Local Differential Privacy (LDP) is attracting industries and researchers as a way of collecting data from individuals while preserving their privacy. LDP provides a strong privacy guarantee; however, it causes a decrease of utility so that we can only apply LDP to simple tasks such as heavy hitter estimation. In this paper, to solve the issue, we explore the amplification of the privacy of LDP with a small loss of utility. In LDP, a privacy parameter which decides the level of privacy protection is treated as the public information or common parameter in a data collection protocol. However, LDP requires that data providers perturb their data on their device, so naturally, data providers can choose and keep their preferred privacy parameter secret. In this paper, we study how the privacy level and utility change in a new privacy model, Parameter Blending Privacy, that the data providers keep their privacy parameter secret. The result concludes that this manipulation amplifies the privacy level with a small loss of utility, so it improves utility-privacy trade-off.