Open AccessPrivacy preservation with unequal data exchange strategy in participatory sensing*
Author(s) -
Tianqi Zhang,
Rong Zhang,
Jin Wang
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1486/5/052004
Subject(s) - upload , participatory sensing , task (project management) , data exchange , computer science , internet privacy , information exchange , data center , center (category theory) , citizen journalism , computer security , data science , world wide web , computer network , engineering , telecommunications , systems engineering , chemistry , crystallography
In participatory sensing, participants contribute sensing data to task center. However, the task center is not always trustworthy. It may try to profile the participants by data mining, which is a great threat to the privacy of participants. To solve this problem, three collaborative data exchange strategies are studied. Participants exchange sensing data before upload to the task center. The mixed data protect the participants’ privacy from data mining by the task center. The simulations show that the unequal data exchange strategy is more efficient than the full exchange strategy because it considers both the tradeoff between privacy preservation and the cost of data exchange.