G
Gabriel Ghinita
Researcher at University of Massachusetts Boston
Publications - 103
Citations - 5248
Gabriel Ghinita is an academic researcher from University of Massachusetts Boston. The author has contributed to research in topics: Differential privacy & Information privacy. The author has an hindex of 31, co-authored 94 publications receiving 4863 citations. Previous affiliations of Gabriel Ghinita include City University of Hong Kong & Fermilab.
Papers
More filters
Proceedings ArticleDOI
Private queries in location based services: anonymizers are not necessary
TL;DR: This work proposes a novel framework to support private location-dependent queries, based on the theoretical work on Private Information Retrieval (PIR), which achieves stronger privacy for snapshots of user locations and is the first to provide provable privacy guarantees against correlation attacks.
Journal ArticleDOI
Preventing Location-Based Identity Inference in Anonymous Spatial Queries
TL;DR: This work proposes transformations based on the well-established K-anonymity concept to compute exact answers for range and nearest neighbor search, without revealing the query source.
Proceedings ArticleDOI
PRIVE: anonymous location-based queries in distributed mobile systems
TL;DR: Prive is proposed, a decentralized architecture for preserving the anonymity of users issuing spatial queries to LBS, which avoids the bottleneck caused by centralized techniques both in terms of anonymizationand location updates.
Journal ArticleDOI
A framework for protecting worker location privacy in spatial crowdsourcing
TL;DR: This paper argues that existing location privacy techniques are not sufficient for SC, and a mechanism based on differential privacy and geocasting that achieves effective SC services while offering privacy guarantees to workers is proposed.
Proceedings Article
Fast data anonymization with low information loss
TL;DR: This paper focuses on one-dimensional (i.e., single attribute) quasi-identifiers, and study the properties of optimal solutions for k-anonymity and l-diversity, and develops efficient heuristics to solve the one- dimensional problems in linear time based on meaningful information loss metrics.