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 correlations …

For protecting users' private data, local differential privacy (LDP) has been leveraged to
provide the privacy-preserving range query, thus supporting further statistical analysis …

Multi-dimensional analytical (MDA) queries are often issued against a fact table with
predicates on (categorical or ordinal) dimensions and aggregations on one or more …

Local Differential Privacy (LDP) allows answering queries on users data while maintaining
their privacy. Queries are often issued on multidimensional datasets with categorical and …

Local Differential Privacy (LDP) provides provable privacy protection for data collection
without the assumption of the trusted data server. Existing mechanisms that satisfy LDP or its …

Local differential privacy (LDP) is a recently proposed privacy standard for collecting and
analyzing data, which has been used, eg, in the Chrome browser, iOS and macOS. In LDP …

Mobile apps and location-based services generate large amounts of location data that can
benefit research on traffic optimization, context-aware notifications and public health (eg …

Marginal tables are the workhorse of capturing the correlations among a set of attributes. We
consider the problem of constructing marginal tables given a set of user's multi-dimensional …

We describe a new algorithm for answering a given set of range queries under $\epsilon $-
differential privacy which often achieves substantially lower error than competing methods …

Local Differential Privacy (LDP) provides provable privacy protection for data collection
without the assumption of the trusted data server. In the real-world scenario, different data …