SpaceTwist: Managing the Trade-Offs Among Location Privacy, Query Performance, and Query Accuracy in Mobile Services

Yiu, Man Lung; Jensen, Christian S.; Huang, Xuegang; Lu, Hua

doi:10.1109/icde.2008.4497445

Cited by 268 publications

(212 citation statements)

References 13 publications

Supporting

Mentioning

212

Contrasting

Order By: Relevance

“…The most common are techniques that resort to private-public dichotomy to justify scenarios where privacy is preserved or threatened [16]. Value based tradeoffs is another privacy justifying framework that does not see privacy as a moral right, but as preference over other values [17]. Thus, the rationale for a mobile usage scenario posing a privacy threat is based on its supports or conflicts with other functionality of the system such as performance or usability.…”

Section: A Contextual Integritymentioning

confidence: 99%

Privacy arguments: Analysing selective disclosure requirements for mobile applications

Tun

Bandara

Price

et al. 2012

2012 20th IEEE International Requirements Engineering Conference (RE)

View full text Add to dashboard Cite

Abstract-Privacy requirements for mobile applications offer a distinct set of challenges for requirements engineering. First, they are highly dynamic, changing over time and locations, and across the different roles of agents involved and the kinds of information that may be disclosed. Second, although some general privacy requirements can be elicited a priori, users often refine them at runtime as they interact with the system and its environment. Selectively disclosing information to appropriate agents is therefore a key privacy management challenge, requiring carefully formulated privacy requirements amenable to systematic reasoning. In this paper, we introduce privacy arguments as a means of analysing privacy requirements in general and selective disclosure requirements (that are both content-and context-sensitive) in particular. Privacy arguments allow individual users to express personal preferences, which are then used to reason about privacy for each user under different contexts. At runtime, these arguments provide a way to reason about requirements satisfaction and diagnosis. Our proposed approach is demonstrated and evaluated using the privacy requirements of BuddyTracker, a mobile application we developed as part of our overall research programme.

show abstract

Section: A Contextual Integritymentioning

confidence: 99%

Privacy arguments: Analysing selective disclosure requirements for mobile applications

Tun

Bandara

Price

et al. 2012

2012 20th IEEE International Requirements Engineering Conference (RE)

View full text Add to dashboard Cite

show abstract

“…So a typical group nearest neighbour query for a group of peers would be "Which is the restaurant that is closest to all of us" or "Find a meeting spot that is within 2 miles of all of us?" Some of the general work in this direction (non-privacypreserving) includes [6], [7] among others. One of the widely referenced works in this area is SpaceTwist [6] which is a protocol where a querying client asks the LBS server to return a set of k points closest to its own location or knearest neighbours.…”

Section: A Location-based Servicesmentioning

confidence: 99%

Privacy Preserving Group Nearest Neighbour Queries in Location-Based Services Using Cryptographic Techniques

Huang

Vishwanathan

2010

2010 IEEE Global Telecommunications Conference GLOBECOM 2010

View full text Add to dashboard Cite

Abstract-Location-based services (LBS) are available on a variety of mobile platforms like cellphones, PDA's, etc. and an increasing number of users subscribe to and use these services. One of the basic privacy issues with LBS is that a user may not necessarily want to disclose their own location whenever they inquire about the location of places of interest to them e.g., nearest gas station, restaurant etc. The privacy aspect of LBS has received attention recently with a number of privacypreserving methodologies being proposed for the client-server model where a querying client requests a location-based server to return some location that is of interest to it without revealing its own location to the server. In this paper, we consider privacy issues in the peer-to-peer model of LBS, where a group of users jointly compute a common location of interest to them such as a restaurant where they could all meet. In such scenarios, all peers in the group would like to jointly find a common location but might not want to reveal their individual locations to each other due to trust issues. We model this problem in the secure multi-party computation framework of cryptography and present a solution where all the peers can jointly compute a common location without the need for any user to reveal its individual location to anyone else. To this end, we present two privacypreserving models and experimentally evaluate the performance of each of them.

show abstract

“…Various obfuscation-based techniques to control the release of location information have been recently proposed (see, e.g., [36,6,7]), based on generalization or perturbation of the precise user's position. One of the first attempts to support privacy in generic context aware systems through obfuscation mechanisms is semantic eWallet [37], an architecture to support context-awareness by means of techniques to retrieve users' context data while enforcing their privacy preferences.…”

Section: Obfuscation Of Context Datamentioning

confidence: 99%

“…Hence, while they may be profitably applied to simple LBS such as nearest neighbor services, it is unlikely that they would be practical for complex context-aware services. On the other hand, obfuscation techniques proposed for LBS (e.g., [6,7]) are specifically addressed to location information; hence, those techniques cannot be straightforwardly applied to other contextual domains. With respect to techniques based on identity anonymity in LBS (e.g., [8,9]) we point out that, since many other kinds of context data besides location may help an adversary in identifying the owner of those data, the amount of context data to be generalized in order to enforce anonymity is large.…”

Section: Introductionmentioning

confidence: 99%

Privacy in Georeferenced Context-Aware Services: A Survey

Riboni

Pareschi

Bettini

2009

Privacy in Location-Based Applications

View full text Add to dashboard Cite

Abstract. Location based services (LBS) are a specific instance of a broader class of Internet services that are predicted to become popular in a near future: context-aware services. The privacy concerns that LBS have raised are likely to become even more serious when several context data, other than location and time, are sent to service providers as part of an Internet request. This paper provides a classification and a brief survey of the privacy preservation techniques that have been proposed for this type of services. After identifying the benefits and shortcomings of each class of techniques, the paper proposes a combined approach to achieve a more comprehensive solution for privacy preservation in georeferenced context-aware services.

show abstract

SpaceTwist: Managing the Trade-Offs Among Location Privacy, Query Performance, and Query Accuracy in Mobile Services

Cited by 268 publications

References 13 publications

Privacy arguments: Analysing selective disclosure requirements for mobile applications

Privacy arguments: Analysing selective disclosure requirements for mobile applications

Privacy Preserving Group Nearest Neighbour Queries in Location-Based Services Using Cryptographic Techniques

Privacy in Georeferenced Context-Aware Services: A Survey

Contact Info

Product

Resources

About