The new Casper: query processing for location services without compromising privacy
Mohamed F. Mokbel,Chi-Yin Chow,Walid G. Aref +2 more
- pp 763-774
TLDR
Zhang et al. as mentioned in this paper presented Casper1, a new framework in which mobile and stationary users can entertain location-based services without revealing their location information, which consists of two main components, the location anonymizer and the privacy-aware query processor.Abstract:
This paper tackles a major privacy concern in current location-based services where users have to continuously report their locations to the database server in order to obtain the service. For example, a user asking about the nearest gas station has to report her exact location. With untrusted servers, reporting the location information may lead to several privacy threats. In this paper, we present Casper1; a new framework in which mobile and stationary users can entertain location-based services without revealing their location information. Casper consists of two main components, the location anonymizer and the privacy-aware query processor. The location anonymizer blurs the users' exact location information into cloaked spatial regions based on user-specified privacy requirements. The privacy-aware query processor is embedded inside the location-based database server in order to deal with the cloaked spatial areas rather than the exact location information. Experimental results show that Casper achieves high quality location-based services while providing anonymity for both data and queries.read more
Citations
More filters
Journal ArticleDOI
Semantic analysis in location privacy preserving
TL;DR: A novel distance measurement that combines the semantic and the Euclidean distance to address the privacy‐preserving issue in the location‐aware applications that services are different among regions is defined.
Book ChapterDOI
Privacy and Anonymity in Location Data Management
Francesco Bonchi,Elena Ferrari +1 more
Book ChapterDOI
Guess-Answer: Protecting Location Privacy in Location Based Services
TL;DR: It is demonstrated that Guess-Answer is an efficient and effective way to achieve personalized privacy through the interaction between user and server, the location information within a region that satisfies the minimum privacy requirement of the user.
Book ChapterDOI
Differential Private Trajectory Obfuscation
TL;DR: This work proposes a novel technique to ensure location privacy for mobility data using differential privacy by injecting noise to both the space and time domain of a spatio-temporal data and presents to the best of the knowledge, the first context aware differential private algorithm.
Patent
A method for preserving privacy within a communication system and an according communication system
TL;DR: In this paper, the location information represented by coordinates of objects and users and/or areas and queries, made to the location-based service, is concealed by transforming of the coordinates.
References
More filters
Journal ArticleDOI
k -anonymity: a model for protecting privacy
TL;DR: The solution provided in this paper includes a formal protection model named k-anonymity and a set of accompanying policies for deployment and examines re-identification attacks that can be realized on releases that adhere to k- anonymity unless accompanying policies are respected.
Proceedings ArticleDOI
Anonymous Usage of Location-Based Services Through Spatial and Temporal Cloaking
Marco Gruteser,Dirk Grunwald +1 more
TL;DR: A middleware architecture and algorithms that can be used by a centralized location broker service that adjusts the resolution of location information along spatial or temporal dimensions to meet specified anonymity constraints based on the entities who may be using location services within a given area.
Journal ArticleDOI
Protecting respondents identities in microdata release
TL;DR: This paper addresses the problem of releasing microdata while safeguarding the anonymity of respondents to which the data refer and introduces the concept of minimal generalization that captures the property of the release process not distorting the data more than needed to achieve k-anonymity.
Journal ArticleDOI
Achieving k -anonymity privacy protection using generalization and suppression
TL;DR: This paper provides a formal presentation of combining generalization and suppression to achieve k-anonymity and shows that Datafly can over distort data and µ-Argus can additionally fail to provide adequate protection.
Journal ArticleDOI
Location privacy in pervasive computing
TL;DR: The mix zone is introduced-a new construction inspired by anonymous communication techniques-together with metrics for assessing user anonymity, based on frequently changing pseudonyms.