Enhancing Privacy in Participatory Sensing Applications with Multidimensional Data

Conference · Sun Jan 01 00:00:00 EST 2012

DOI:https://doi.org/10.1109/PerCom.2012.6199861· OSTI ID:1037644

Groat, Michael ^[1]; Forrest, Stephanie ^[1]; Horey, James L ^[2]; Edwards, Benjamin ^[1]; He, Wenbo ^[3]

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1. University of New Mexico, Albuquerque
2. ORNL
3. University of Nebraska, Lincoln

Participatory sensing applications rely on individuals to share local and personal data with others to produce aggregated models and knowledge. In this setting, privacy is an important consideration, and lack of privacy could discourage widespread adoption of many exciting applications. We present a privacy-preserving participatory sensing scheme for multidimensional data which uses negative surveys. Multidimensional data, such as vectors of attributes that include location and environment fields, pose a particular challenge for privacy protection and are common in participatory sensing applications. When reporting data in a negative survey, an individual participant randomly selects a value from the set complement of the sensed data value, once for each dimension, and returns the negative values to a central collection server. Using algorithms described in this paper, the server can reconstruct the probability density functions of the original distributions of sensed values, without knowing the participants actual data. As a consequence, complicated encryption and key management schemes are avoided, conserving energy. We study trade-offs between accuracy and privacy, and their relationships to the number of dimensions, categories, and participants. We introduce dimensional adjustment, a method that reduces the magnification of error associated with earlier work. Two simulation scenarios illustrate how the approach can protect the privacy of a participant's multidimensional data while allowing useful population information to be aggregated.

• Research Organization: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE Laboratory Directed Research and Development (LDRD) Program
• DOE Contract Number: AC05-00OR22725
• OSTI ID: 1037644
• Resource Relation: Conference: IEEE International Conference on Pervasive Computing and Communications (IEEE PERCOM), Lugano, Switzerland, 20120319, 20120319
• Country of Publication: United States
• Language: English