Privacy in Location-Based Applications: Research Issues and by Claudio Bettini, Sushil Jajodia, Pierangela Samarati, Sean

By Claudio Bettini, Sushil Jajodia, Pierangela Samarati, Sean X. Wang

Location-based purposes check with those who use situation facts in a well-liked demeanour. position info will be very potent for carrier provisioning, allowing the beginning of a brand new new release of data providers. even if facts safeguard and privateness matters were commonly investigated in numerous domain names, present ideas will not be quite simply appropriate to location-based functions. Conciliating the effectiveness of those purposes with privateness matters constitutes a different problem, commonly as a result semantic richness of position and time details. study during this box includes elements of spatio-temporal reasoning, question processing, method safety, statistical inference, and extra importantly, anonymization suggestions. a number of examine teams were operating lately to spot privateness assaults and security innovations during this domain.

This state of the art survey offers a superior flooring for researchers impending this subject to appreciate present achievements via a typical categorization of privateness threats and security options. This target is very hard contemplating the categorical (and usually implicit) assumptions that signify the hot literature on privateness in location-based services.

The publication additionally illustrates the numerous elements that make the examine of this subject a very attention-grabbing learn topic, together with themes that transcend privateness retaining variations of carrier requests, and comprise entry keep an eye on, privateness conserving publishing of relocating item info, privateness within the use of particular positioning know-how, and privateness in vehicular community applications.

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Extra resources for Privacy in Location-Based Applications: Research Issues and Emerging Trends

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Result Quality Assume that an ILRQ from user S is partitioned into B subqueries that correspond to B locations among A1 to AB . S. Jensen, H. L. Yiu at Ak (1 ≤ k ≤ B) be pk (S). The result of the subquery at Ak is Rk , while B R = k=1 Rk . The quality score of the ILRQ is defined as follows: B pk (S) · Query score = k=1 |Rk | |R| The score varies between 0, the lowest quality, and 1, the highest quality. When a user S receives a query result and its corresponding score, she can adjust privacy preferences stored in the cloaking agent according to her expectation and the score value.

Spacetwist: Managing the trade-offs among location privacy, query performance, and query accuracy in mobile services. In: Proc. of the 24th International Conference on Data Engineering, pp. 366–375. IEEE Computer Society, Los Alamitos (2008) 30. : Private queries in location based services: Anonymizers are not necessary. In: Proc. of SIGMOD, pp. 121–132. ACM Press, New York (2008) 31. : Privacy-aware proximity based services. In: Proc. of the 10th International Conference on Mobile Data Management, pp.

Yiu Table 1. Features of Various Location Privacy Techniques Method Domain Privacy Space Measure [3] Euclidean Area-based [4] Euclidean Area-based [5, 6] Euclidean Area-based [7] Euclidean Area-based [8] Euclidean Size-based [9] Euclidean Size and Area [10, 11] Network Size-based [12] Euclidean Distance-based [12]g Euclidean Distance-based [13] Euclidean Full-domain [14] Euclidean Full-domain Supported Exact Accuracy Impl. Queries Result Guarantee Difficulty Range Yes Yes Medium Range, kNN Yes Yes Medium Range, kNN Yes Yes Medium Proximity No No Medium Range, kNN Yes Yes Low Range, kNN Yes Yes Low 1NN Yes/No Yes/No Medium kNN Yes Yes Low kNN No Yes Low kNN No No Medium 1NN Yes Yes High exact results can be retrieved, (v) whether result accuracy guarantees are given (for approximate results), and (vi) the difficulty of implementing the solution.

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