Processing private queries over untrusted data cloud through privacy homomorphism

Hu, Haibo; Xu, Jianliang; Ren, Chushi; Choi, Byron

doi:10.1109/icde.2011.5767862

Cited by 177 publications

(120 citation statements)

References 29 publications

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“…For example, N1's MBB overlaps the lower-left 16 cells, so map(N1.mbb) = [0, 15]. The query Q" is also mapped to a value range, which is [4,11] in this example. With these ranges, the boundary verifications of N2 and N3 can be carried out on their 1D mapped values, instead of their 2D MBBs.…”

Section: Linear Ordering and Embeddingmentioning

confidence: 99%

“…As for location privacy, the literature of mobile computing and spatial databases extensively investigates this problem in various research domains, including query processing [12,1,8,6,23,20,11], message communication [7,25], and location data publishing [24,10]. In most works, location cloaking has been the predominant technique of privacy protection.…”

Section: Background and Related Workmentioning

confidence: 99%

“…However, it only protects privacy conditionally against certain privacy metrics, such as k-anonymity. Except for very few works [8,23,20,11], unconditionally protecting user locations by disclosing nothing about them is an unprecedented task. Our work is the first of this kind on query authentication.…”

Section: Background and Related Workmentioning

confidence: 99%

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Authenticating location-based services without compromising location privacy

Chang

et al. 2012

Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data

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The popularity of mobile social networking services (mSNSs) is propelling more and more businesses, especially those in retailing and marketing, into mobile and location-based forms. To address the trust issue, the service providers are expected to deliver their location-based services in an authenticatable manner, so that the correctness of the service results can be verified by the client. However, existing works on query authentication cannot preserve the privacy of the data being queried, which are sensitive user locations when it comes to location-based services and mSNSs. In this paper, we address this challenging problem by proposing a comprehensive solution that preserves unconditional location privacy when authenticating range queries. Three authentication schemes for R-tree and grid-file index, together with two optimization techniques, are developed. Cost models, security analysis, and experimental results consistently show the effectiveness, reliability and robustness of the proposed schemes under various system settings and query workloads.

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Section: Linear Ordering and Embeddingmentioning

confidence: 99%

Section: Background and Related Workmentioning

confidence: 99%

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Authenticating location-based services without compromising location privacy

Chang

et al. 2012

Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data

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“…There are two methods for protecting location or point of interest in the specific area of user request for location tagged item. The main goal here is apply the requesting (query), the clients to extract their near point of interest with the confidentiality of user location the first related work is to implementation using Cloaking regions [8,9], but the majority of the solutions using the relating anonymity method and this not that much protective outliers [3,4,5]. The second approach related to proposed work is to use private information retrieval (P.I.R) [6,7].…”

Section: Related Workmentioning

confidence: 99%

Efficient K-Nearest Neighbours Discovery using Complex Secure Evaluation Function in Geo Tagged Dataset

Manoj¹,

Rao²

2015

IJCA

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The materialization of mobile equipment with fast Internet connectivity and geo-tagged capabilities has led to a revolution in personalized position-based services where clients are enabled to admittance information about points of interest that are pertinent to their interests and are also close to their geo tagged coordinates. The important type of queries that absorb location attributes is symbolized by nearestneighbor queries, where a client wants to retrieve the kPoint of interests that are nearest to the user's current location. Entities specialized in various areas of interest and gather large amounts of geo-tagged data that appeal to subscribed users. In this paper the proposes development and implementation of frame work to provide more complex protected evaluation method on cipher texts, like skyline queries and security protection guarantees against the client, to prevent it from learning anything other than the received k query results. Keywordspoints of interest (POI), geo-tagged data, Query processing, and KNN Query.

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“…The goal of cloud computing is to provide high performance computing or super computing power with the cloud computing technologies, large pool of resources can be connected using public or private network. Cloud is maintaining huge amount of resources hence security and privacy are two main concepts which is to be preserved [15]. There are three different types of cloud computing.…”

Section: Introductionmentioning

confidence: 99%

Preserving Data Confidentiality and Query Privacy Using KNNR Approach

Murthy¹

2015

IJRITCC

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Cloud computing is one of the famous and well known technique that processes the data query efficiently. Since it is maintaining huge amount of resources, its privacy and security is an issue. Cloud service providers are not trust worthy, so data is to be s ecured. Whenever the data is sent to the cloud, it is encrypted because to protect the sensitive data such that query privacy and data confidentiality is assured. Cloud computing reduces the in-house resources .This doesn't mean processing of the query should be slow. To ensure query privacy and data confidentiality RASP approach is designed. The RASP Perturbation technique combines Order preserving Encryption, Dimensionality Expansion, random noise injection , random projection to provide strong safety to the perturbed data and query. RASP make use of the kNN algorithm to process the query efficiently. kNN approach use the minimum square range to process the query. It transfers data to the multidimensional space where it uses indexing approach to process the minimum square range points. __________________________________________________*****_________________________________________________ 1.IntroductionCloud computing refers to the service which is accessed over the internet. It is based on pay-as-you-go manner. The goal of cloud computing is to provide high performance computing or super computing power with the cloud computing technologies, large pool of resources can be connected using public or private network. Cloud is maintaining huge amount of resources hence security and privacy are two main concepts which is to be preserved [15]. There are three different types of cloud computing.Infrastructure as a service where hardware is accessed over the internet such as server or storage.Software as a service where complete application is running on other's computer can be accessed such as web based email and Google document is well known exam which offer many online application.Platform as a service means that the application can be developed using web based tools so they run on system software and hardware. Force.com and the Google map application are examples.Parallel computing of query service in the cloud is very popular because of the advantages of scalability and cost saving. Using the cloud infrastructure, the cloud service providers/ owners can conveniently scale -up and down and pay for what they use. Cloud service providers are not trust worthy and hence the data confidentiality and query privacy should be preserved .The new approach should be proposed to preserve the privacy of data resources. To enjoy the benefits of the cloud computing, it is not meaningful to provide slow query services, because of security and privacy issues. The main purpose of cloud is to reduce the significant amount of cloud resources [4]. There exist some co-ordination between the data privacy, query privacy, use of the cloud. This is referred as DQEL criteria: Data privacy, query privacy , efficient query processing and low processing cost [9]. Definition of RA...

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Processing private queries over untrusted data cloud through privacy homomorphism

Cited by 177 publications

References 29 publications

Authenticating location-based services without compromising location privacy

Authenticating location-based services without compromising location privacy

Efficient K-Nearest Neighbours Discovery using Complex Secure Evaluation Function in Geo Tagged Dataset

Preserving Data Confidentiality and Query Privacy Using KNNR Approach

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