Enabling Probabilistic Differential Privacy Protection for Location Recommendations
Related Research Unit(s)
|Journal / Publication||IEEE Transactions on Services Computing|
|Publication status||Online published - 5 Mar 2018|
|Link to Scopus||https://www.scopus.com/record/display.uri?eid=2-s2.0-85042886080&origin=recordpage|
The sequential pattern in the human movement is one of the most important aspects for location recommendations in geosocial networks. Existing location recommenders have to access users’ raw check-in data to mine their sequential patterns that raises serious location privacy breaches. In this paper, we propose a new Privacy-preserving LOcation REcommendation framework (PLORE) to address this privacy challenge. First, we employ the n th-order additive Markov chain to exploit users’ sequential patterns for location recommendations. Further, we contrive the probabilistic differential privacy mechanism to reach a good trade-off between high recommendation accuracy and strict location privacy protection. Finally, we conduct extensive experiments to evaluate the performance of PLORE using three large-scale real-world data sets. Extensive experimental results show that PLORE provides efficient and highly accurate location recommendations, and guarantees strict privacy protection for user check-in data in geosocial networks.
- additive Markov chain, Location recommendations, noise injection, probabilistic differential privacy, sequential patterns
IEEE Transactions on Services Computing, 05.03.2018.
Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal