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SENSYS
2010
ACM

Privacy-aware regression modeling of participatory sensing data

8 years 10 months ago
Privacy-aware regression modeling of participatory sensing data
Many participatory sensing applications use data collected by participants to construct a public model of a system or phenomenon. For example, a health application might compute a model relating exercise and diet to amount of weight loss. While the ultimately computed model could be public, the individual input and output data traces used to construct it may be private data of participants (e.g., their individual food intake, lifestyle choices, and resulting weight). This paper proposes and experimentally studies a technique that attempts to keep such input and output data traces private, while allowing accurate model construction. This is significantly different from perturbation-based techniques in that no noise is added. The main contribution of the paper is to show a certain data transformation at the client side that helps keeping the client data private while not introducing any additional error to model construction. We particularly focus on linear regression models which are w...
Hossein Ahmadi, Nam Pham, Raghu K. Ganti, Tarek F.
Added 15 Feb 2011
Updated 15 Feb 2011
Type Journal
Year 2010
Where SENSYS
Authors Hossein Ahmadi, Nam Pham, Raghu K. Ganti, Tarek F. Abdelzaher, Suman Nath, Jiawei Han
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