An architecture for privacy-sensitive ubiquitous computing

By Jason I. Hong, James A. Landay. ACM SIGMOBILE International Conference on Mobile Systems, Applications, and Services, 2004

682 citations recorded by the source corpus.

Privacy is the most often-cited criticism of ubiquitous computing, and may be the greatest barrier to its long-term success. However, developers currently have little support in designing software architectures and in creating interactions that are effective in helping end-users manage their privacy. To address this problem, we present Confab, a toolkit for facilitating the development of privacy-sensitive ubiquitous computing applications. The requirements for Confab were gathered through an analysis of privacy needs for both end-users and application developers. Confab provides basic support for building ubiquitous computing applications, providing a framework as well as several customizable privacy mechanisms. Confab also comes with extensions for managing location privacy. Combined, these features allow application developers and end-users to support a spectrum of trust levels and privacy needs.

Subfields: Computer Science Applications, Computer Vision and Pattern Recognition, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/b8fbc4b35d62373787874eadcbb931f306913b33

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Citations in the corpus

Engineering Privacy (2009, background): “These systems embed privacy features and create privacy-by-architecture, providing higher levels of privacy friendliness than systems that collect personally identifiable data and adhere to a FIP policy [84].”
Computing with Spatial Trajectories (2011, background): “To achieve this goal, [28] delegate part of the path extraction process to objects, by assigning to them adaptive lightweight filters that dynamically suppress unnecessary location updates and, , and thus, help reduce the communication overhead.”
Recruitment Framework for Participatory Sensing Data Collections (2010, methodology): “Also, our system is designed to be run in coordination with a personal data vault where all participant information is stored and external queries on this data are strictly opt-in [10,11].”
Location disclosure to social relations: why, when, & what people want to share (2005, background): “Regarding steps 3 and 4 of the decision process, the participants’ responses seem closely related to a phenomenon called recipient design [21] in conversation analysis where people carefully construct what they say “to match the needs and capabilities of recipients, taking account of such varied fac”
Precision: Privacy Enhanced Context-Aware Information Fusion in Ubiquitous Healthcare (2007, background): “Confab [8] offers a framework where personal information is captured, stored and processed on the end-user’s computer as much as possible.”
Extracting a Mobility Model from Real User Traces (2006, background): “Researchers who want to simulate how users aggregate (e.g., a friend-finder application [ 8 ]) need to have this type of model.”
Over-exposed?: privacy patterns and considerations in online and mobile photo sharing (2007, background): “The privacy issues with mobile and networked devices have been explored for networked desktops [6,10], wireless devices [11, 17 ], mobile phones [3] as well as sensor networks [12].”
A Formal Model of Obfuscation and Negotiation for Location Privacy (2005, background): “[14] refer to landmarks and other significant locations instead of coordinate-based geographic information within the Confab system.”
The new Casper: query processing for location services without compromising privacy (2006, background): “Motivated by the privacy threats of location-detection devices [1, 6, 8, 44], several research eﬀorts are dedicated to protect the user location privacy (e.g., false dummies [27], landmark objects [21], and location perturbation [13, 16, 17]).”