Privacy in location-aware systems

Freytag, Johann-Christoph

doi:10.1145/1567253.1567255

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…The general topic of privacy for location-based services is of considerable interest in GIS (e.g., see [11,12,17,23,30,34]). Of all the problems listed above, the convex hull and nearest-neighbor problems are probably the most well-motivated for geographic data.…”

Section: Related Prior Workmentioning

confidence: 99%

Privacy-preserving data-oblivious geometric algorithms for geographic data

Eppstein

Goodrich

Tamassia

2010

Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems

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We give efficient data-oblivious algorithms for several fundamental geometric problems that are relevant to geographic information systems, including planar convex hulls and all-nearest neighbors. Our methods are "data-oblivious" in that they don't perform any data-dependent operations, with the exception of operations performed inside low-level blackbox circuits having a constant number of inputs and outputs. Thus, an adversary who observes the control flow of one of our algorithms, but who cannot see the inputs and outputs to the blackbox circuits, cannot learn anything about the input or output. This behavior makes our methods applicable to secure multiparty computation (SMC) protocols for geographic data used in location-based services. In SMC protocols, multiple parties wish to perform a computation on their combined data without revealing individual data to the other parties. For instance, our methods can be used to solve a problem posed by Du and Atallah, where Alice has a set, A, of m private points in the plane, Bob has another set, B, of n private points in the plane, and Alice and Bob want to jointly compute the convex hull of A ∪ B without disclosing any more information than what can be derived from the answer. In particular, neither Alice nor Bob want to reveal any of their respective points that are in the interior of the convex hull of A ∪ B.

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Section: Related Prior Workmentioning

confidence: 99%

Privacy-preserving data-oblivious geometric algorithms for geographic data

Eppstein

Goodrich

Tamassia

2010

Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems

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Prihook: Differentiated Context-Aware Hook Placement for Different Owners' Smartphones

Tian

Wang

Liu

et al. 2020

2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

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A hook is a piece of code. It checks user privacy policy before some sensitive operations happen. We propose an automated solution named Prihook for hook placement in the Android Framework. Addressing specific context-aware user privacy concerns, the hook placement in Prihook is personalized. Specifically, we design User Privacy Preference Table (UPPT) to help a user express his privacy concerns. And we leverage machine learning to discover a Potential Method Set (consisting of Sensor Data Access Methods and Sensor Control Methods) from which we can select a particular subset to put hooks. We propose a mapping from words in the UPPT lexicon to methods in the Potential Method Set. With this mapping, Prihook is able to (a) select a specific set of methods; and (b) generate and place hooks automatically. We test Prihook separately on 6 typical UPPTs representing 6 kinds of resource-sensitive UPPTs, and no user privacy violation is found. The experimental results show that the hooks placed by PriHook have small runtime overhead.

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Privacy in location-aware systems

Cited by 2 publications

References 9 publications

Privacy-preserving data-oblivious geometric algorithms for geographic data

Privacy-preserving data-oblivious geometric algorithms for geographic data

Prihook: Differentiated Context-Aware Hook Placement for Different Owners' Smartphones

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