Privately querying location-based services with SybilQuery

Shankar, Pravin; Ganapathy, Vinod; Iftode, Liviu

doi:10.1145/1620545.1620550

Cited by 111 publications

(84 citation statements)

References 32 publications

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“…Existing approaches for user query anonymization in LBS can be roughly grouped into the following five classes: (1) Cloaking 2-5, 11-13 , (2) Obfuscation 14, 15 (3) False Locations 4, 23 (4) Space Transformation [6][7] , and (5) Dummies [16][17][18] .…”

Section: Related Workmentioning

confidence: 99%

CSEP: Circular Shifting Encryption Protocols for Location Privacy Protection

Chen¹,

Binglin²,

Li³

et al. 2017

IJNDC

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Location Based Service (LBS) is gaining popularity. As one fundamental LBS service, range search returns all Point of Interests (POIs) within a user-specified range. However, people leave their location privacy at risks when using range search. How to provide a high-quality range search service while protecting users' location privacy is a challenging problem. Most existing approaches use space-filling curves and cloaked region method to provide privacy-preservation location services, but these methods cannot return the accurate results. In this paper, we propose a set of Circular Shifting Encryption Protocols (CSEP) based on homomorphism and circular shift for location privacy protection of range search. CSEP leverages homomorphism encryption to encrypt users' locations, and LBS servers compute distances on cyphertext. In this way, LBS server can return POIs within the specified range, while learning nothing about the user's real location. To accommodate the different query range and the private protection degree of users, we propose a circular shifting encryption method to reduce the redundancy and increase the degree of privacy protection. We implement a prototype of CSEP, and evaluate it with real POI set of a large-scale production LBS. Experimental results show that CSEP can provide reliable privacy protection and accurate range search, with reasonable compute overhead and communication overhead.

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Section: Related Workmentioning

confidence: 99%

CSEP: Circular Shifting Encryption Protocols for Location Privacy Protection

Chen¹,

Binglin²,

Li³

et al. 2017

IJNDC

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show abstract

“…Usually, these approaches need a Trusted Third Party (TTP). SybilQuery (Shankar, Ganapathy, & Iftode, 2009) is an example of this approach.…”

Section: Position Dummiesmentioning

confidence: 99%

Improved Recommender for Location Privacy Preferences

Hadi¹,

Cazalas²

2015

CIS

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Location-based services are one of the fastest growing technologies. Millions of users are using these services and sharing their locations using their smart devices. The popularity of using such applications, while enabling others to access user's location, brings with it many privacy issues. The user has the ability to set his location privacy preferences manually. Many users face difficulties in order to set their preferences in the proper way. One solution is to use machine learning based methods to predict location privacy preferences automatically. These models suffer from degraded performance when there is no sufficient training data. Another solution is to make the decision for the intended user, depending on the collected opinions from similar users. User-User Collaborative Filtering (CF) is an example within this category. In this paper, we will introduce an improved machine learning based predictor. The results show significant improvements in the performance. The accuracy was improved from 75.30% up to 84.82%, while the privacy leak was reduced from 11.75% up to 7.65%. We also introduced an integrated model which combines both machine learning based methods and collaborative filtering based methods in order to get the advantages from both of them.

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“…This technique provide a median resolution of 125 meters, and does not protect users' privacy against the location broker. Several papers have followed the work of Grueteser et al (see, e.g., [2,24,32,39]). …”

Section: Related Workmentioning

confidence: 99%

Privacy-preserving distance computation and proximity testing on earth, done right

Šeděnka

Gasti

2014

Proceedings of the 9th ACM Symposium on Information, Computer and Communications Security

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In recent years, the availability of GPS-enabled smartphones have made location-based services extremely popular. A multitude of applications rely on location information to provide a wide range of services. Location information is, however, extremely sensitive and can be easily abused. In this paper, we introduce the first protocols for secure computation of distance and for proximity testing over a sphere. Our secure distance protocols allow two parties, Alice and Bob, to determine their mutual distance without disclosing any additional information about their location. Through our secure proximity testing protocols, Alice only learns if Bob is in close proximity, i.e., within some arbitrary distance.Our techniques rely on three different representations of Earth, which provide different trade-offs between accuracy and performance. We show, via experiments on a prototype implementation, that our protocols are practical on resourceconstrained smartphone devices. Our distance computation protocols runs, in fact, in 54 to 78 ms on a commodity Android smartphone. Similarly, our proximity tests require between 1.2 s and 2.8 s on the same platform. The imprecision introduced by our protocols is very small, i.e., between 0.1% and 3% on average, depending on the distance.

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Privately querying location-based services with SybilQuery

Cited by 111 publications

References 32 publications

CSEP: Circular Shifting Encryption Protocols for Location Privacy Protection

CSEP: Circular Shifting Encryption Protocols for Location Privacy Protection

Improved Recommender for Location Privacy Preferences

Privacy-preserving distance computation and proximity testing on earth, done right

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