Blind Evaluation of Nearest Neighbor Queries Using Space Transformation to Preserve Location Privacy

Khoshgozaran, Ali; Shahabi, Cyrus

doi:10.1007/978-3-540-73540-3_14

Cited by 223 publications

(188 citation statements)

References 17 publications

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“…A similar problem on kNN computation at an untrusted platform is studied for location based services (LBS) [8,17,9,13], where users submit queries to an untrusted server which holds the data. The focus of such applications is on protecting the privacy of users (query content), since the database is assumed to be owned by the server [9].…”

Section: Knn On Sconedb Modelmentioning

confidence: 99%

“…Though the safety level of DPT is not high, we remark that DPT is useful in situations where only level-1 attacks are concerned. kNN computation at an untrusted platform is also considered in location-based service (LBS) systems [8,17,9,13]. In LBS models, a server holds a set of tuples namely points of interest (POI).…”

Section: Related Workmentioning

confidence: 99%

“…This requires additional effort at the user's side which may be considerable for light-weight clients. An LBS model that performs encryption for the purpose of kNN search is proposed in [13]. The main idea is to use a Hilbert curve to 'encrypt' database points and the queries.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Secure kNN computation on encrypted databases

Wong

Cheung

Kao

et al. 2009

Proceedings of the 2009 ACM SIGMOD International Conference on Management of Data

730

483

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Service providers like Google and Amazon are moving into the SaaS (Software as a Service) business. They turn their huge infrastructure into a cloud-computing environment and aggressively recruit businesses to run applications on their platforms. To enforce security and privacy on such a service model, we need to protect the data running on the platform. Unfortunately, traditional encryption methods that aim at providing "unbreakable" protection are often not adequate because they do not support the execution of applications such as database queries on the encrypted data. In this paper we discuss the general problem of secure computation on an encrypted database and propose a SCONEDB (Secure Computation ON an Encrypted DataBase) model, which captures the execution and security requirements. As a case study, we focus on the problem of k-nearest neighbor (kNN) computation on an encrypted database. We develop a new asymmetric scalar-product-preserving encryption (ASPE) that preserves a special type of scalar product. We use APSE to construct two secure schemes that support kNN computation on encrypted data; each of these schemes is shown to resist practical attacks of a different background knowledge level, at a different overhead cost. Extensive performance studies are carried out to evaluate the overhead and the efficiency of the schemes.

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Section: Knn On Sconedb Modelmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Secure kNN computation on encrypted databases

Wong

Cheung

Kao

et al. 2009

Proceedings of the 2009 ACM SIGMOD International Conference on Management of Data

730

483

View full text Add to dashboard Cite

show abstract

“…Various privacy protection algorithms proposed for data privacy have been adopted for protecting location privacy of mobile users. The types of privacy protection algorithms include anonymization [1,5,8,10,13,20], data suppression [18], trajectory inference prevention [2,3,[14][15][16] and encryption [11]. While most of the existing schemes are aimed at preventing the adversary from distinguishing the location of a given user from that of other users, their perturbation techniques are mostly unidirectional and lack the ability to de-anonymize the perturbed information even when a user accessing the information has suitable credentials for obtaining finer information.…”

Section: Related Workmentioning

confidence: 99%

De-anonymizable Location Cloaking for Privacy-Controlled Mobile Systems

Palanisamy

2015

Network and System Security

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Abstract. The rapid technology upgrades of mobile devices and the popularity of wireless networks significantly drive the emergence and development of Location-based Services (LBSs), thus greatly expanding the business of online services and enriching the user experience. However, the personal location data shared with the service providers also leave hidden risks on location privacy. Location anonymization techniques transform the exact location of a user into a cloaking area by including the locations of multiple users in the exposed area such that the exposed location is indistinguishable from that of the other users. However in such schemes, location information once perturbed cannot be recovered from the cloaking region and as a result, users of the location cannot obtain fine granular information even when they have access to it. In this paper, we propose Dynamic Reversible Cloaking (DRC) a new de-anonymziable location cloaking mechanism that allows to restore the actual location from the perturbed information through the use of an anonymization key. Extensive experiments using realistic road network traces show that the proposed scheme is efficient, effective and scalable.

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“…[13] ignores non-querying users, and instead groups only querying users among themselves. [14] proposes a location privacy method specifically for approximate NN processing. In [15,16] the user u forwards to the LBS a set of dummy locations in addition to his/her own.…”

Section: Alternative Location Obfuscation Approachesmentioning

confidence: 99%

Spatial Cloaking Revisited: Distinguishing Information Leakage from Anonymity

Tan

Lin

Mouratidis

2009

Advances in Spatial and Temporal Databases

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Abstract. Location-based services (LBS) are receiving increasing popularity as they provide convenience to mobile users with on-demand information. The use of these services, however, poses privacy issues as the user locations and queries are exposed to untrusted LBSs. Spatial cloaking techniques provide privacy in the form of k-anonymity; i.e., they guarantee that the (location of the) querying user u is indistinguishable from at least k-1 others, where k is a parameter specified by u at query time. To achieve this, they form a group of k users, including u, and forward their minimum bounding rectangle (termed anonymizing spatial region, ASR) to the LBS. The rationale behind sending an ASR instead of the distinct k locations is that exact user positions (querying or not) should not be disclosed to the LBS. This results in large ASRs with considerable dead-space, and leads to unnecessary performance degradation. Additionally, there is no guarantee regarding the amount of location information that is actually revealed to the LBS. In this paper, we introduce the concept of information leakage in spatial cloaking. We provide measures of this leakage, and show how we can trade it for better performance in a tunable manner. The proposed methodology directly applies to centralized and decentralized cloaking models, and is readily deployable on existing systems.

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Blind Evaluation of Nearest Neighbor Queries Using Space Transformation to Preserve Location Privacy

Cited by 223 publications

References 17 publications

Secure kNN computation on encrypted databases

Secure kNN computation on encrypted databases

De-anonymizable Location Cloaking for Privacy-Controlled Mobile Systems

Spatial Cloaking Revisited: Distinguishing Information Leakage from Anonymity

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