Enabling context-aware and privacy-conscious user data sharing

Hull, Richard; Kumar, Bharat; Lieuwen, Daniel F.; Patel‐Schneider, Peter F.; Sahuguet, Arnaud; Varadarajan, Sriram; Vyas, Anand B.

doi:10.1109/mdm.2004.1263065

Cited by 80 publications

(55 citation statements)

References 6 publications

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“…However, the richness and dynamics of contextual situations that may occur in pervasive and mobile computing environments claim for the definition of formal languages to express complex conditions on a multitude of context data, as well as sufficiently expressive languages to represent the context itself. To this aim, Houdini [28] provides a comprehensive formal framework to represent dynamic context data, integrate them from heterogeneous sources, and share context information on the basis of users' privacy policies. In particular, privacy policies can be expressed considering the context of the data owner (i.e., the user) and the context of the subject.…”

Section: Network and Cryptographic Protocolsmentioning

confidence: 99%

Privacy in Georeferenced Context-Aware Services: A Survey

Riboni

Pareschi

Bettini

2009

Privacy in Location-Based Applications

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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.

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Section: Network and Cryptographic Protocolsmentioning

confidence: 99%

Privacy in Georeferenced Context-Aware Services: A Survey

Riboni

Pareschi

Bettini

2009

Privacy in Location-Based Applications

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“…The Houdini framework [4] focuses on the expression and enforcement of individual privacy preferences in a cellular network. One of Houdini's innovations is the use of Web-based forms for defining various generic activity-oriented context attributes, based on a combination of raw location and time data.…”

Section: Related Workmentioning

confidence: 99%

“…The CPE privacy model specifies not only who can access what data, but also for what purpose and under what context. A variety of prior work (e.g., [3] [4]) has explored the use of ACL-based privacy systems. However, CPE addresses unique challenges specific to a large enterprise domain where large number of concurrent access is a requirement.…”

Section: Introductionmentioning

confidence: 99%

Privacy Engine for Context-Aware Enterprise Application Services

Blount

Davis²,

Ebling

et al. 2008

2008 IEEE/IFIP International Conference on Embedded and Ubiquitous Computing

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“…In some of our past work, we had assumed that the user would provision preferences (targeted toward a single application), which along with other subscriber data could then be interpreted at run time by a high-speed Alcatel-Lucent Vortex ® [11] rules engine to personalize the application behavior. Though useful, this puts an undue burden on the user; the user needs to provision his preferences explicitly and keep them up-to-date as his habits and intentions change.…”

Section: Introductionmentioning

confidence: 99%

Intuitive network applications: Learning user context and behavior

Bila¹,

Cao²,

Dinoff³

et al. 2008

Bell Labs Tech. J.

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There are various dimensions involved in this approach: 1) machine learning techniques to gather user habits and preferences in an automated fashion; 2) human factors-directed approaches to explicitly gather additional information from the user, while minimizing the (perceived) inconvenience to him; 3) providing the user with an easy mechanism to view and control the acquired information (whether learned or explicitly gathered); and 4) ensuring that the overall information monitoring and analysis are done in a robust, scalable fashion, and the acquired information can be used at run time in a near-real time fashion. In this paper, we focus primarily on an overview of the INA architecture and present some details on machine learning directed at an advertising

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Enabling context-aware and privacy-conscious user data sharing

Cited by 80 publications

References 6 publications

Privacy in Georeferenced Context-Aware Services: A Survey

Privacy in Georeferenced Context-Aware Services: A Survey

Privacy Engine for Context-Aware Enterprise Application Services

Intuitive network applications: Learning user context and behavior

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