Enabling Probabilistic Differential Privacy Protection for Location Recommendations

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

7 Scopus Citations
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Original languageEnglish
Pages (from-to)426-440
Journal / PublicationIEEE Transactions on Services Computing
Issue number2
Online published5 Mar 2018
Publication statusPublished - Mar 2021


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 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.

Research Area(s)

  • additive Markov chain, Location recommendations, noise injection, probabilistic differential privacy, sequential patterns