Personalized Privacy-Preserving Data Aggregation for Histogram Estimation

Abstract

Histogram estimation is one of the fundamental tasks in crowdsourcing data aggregation. Since contributing data reveal more or less information about individuals' identifications and activities, participants need to preserve privacy of data according to their own levels of privacy concern. However, most of the existing work only aggregates data with an identical privacy level. In this paper, we propose an aggregation scheme for histogram estimation, wherein participants can publish their data at personalized differential-privacy levels. The aggregator also benefits from potential wider engagement or more honest data. Specially, since privacy levels under personalized privacy policy are sensitive information for participants, our scheme permits participants to keep their privacy levels secret even from the aggregator. We also show how to further optimize the estimation accuracy under given privacy levels by choosing specific randomization strategies.

A Comprehensive Survey on Local Differential Privacy toward Data Statistics and Analysis

“…It can easily achieve personalized/granular privacy protection. Some existing work [ 57 , 203 ] aimed to propose personalized LDP-based frameworks for private histogram estimation. Gu et al [ 59 ] presented Input-Discriminative LDP (ID-LDP) that is a fine-grained privacy notion and reflects the distinct privacy requirements of different inputs.…”

PLDP: Personalized Local Differential Privacy for Multidimensional Data Aggregation

“…An inputdiscriminative unary encoding is designed to perturb the attribute value with the specified budgets. In [17,18], the server provides several privacy budgets and owner can choose one budget for himself to perturb his data. en, both the perturbed data and the selected budget are reported to the server.…”

“…In [19], it is the data owner who decides the privacy budget for his own data; however, it still needs to report the privacy budget to the server for frequency estimation. e schemes [17][18][19] have tried to consider the personal privacy requirement, but all of them are designed for one-dimensional data. In addition, the server needs to know the privacy budgets that are applied to the disturbed data to estimate the data distribution, which would also expose privacy to the server.…”

Personalized Differential Privacy Preserving Data Aggregation for Smart Homes

“…The algorithm of privacy protection is not performed on local clients. Wang et al [5] introduced a data aggregation scheme that provides personalized privacy preservation for participants. However, the scheme can't satisfy the need for real-time and multiple times data collection, and it also introduces too much noise.…”

“…For instance, smart homes can analyze the preferential temperature distribution utilizing the collected data, further ascertain the most commonplace temperature; in order to deliver appropriate services, it needs to discover the age structure of the residents. Nevertheless, dwellers' privacy requirement has been a vast obstacle to the widespread participation of contributing data to the collector [5]. Users would not like to share their sensitive information unless their privacy issues have been resolved appropriately.…”