A Utility-Aware General Framework With Quantifiable Privacy Preservation for Destination Prediction in LBSs

• Published in: IEEE/ACM transactions on networking Vol. 29; no. 5; pp. 2228 - 2241
• Main Authors: Jiang, Hongbo, Wang, Mengyuan, Zhao, Ping, Xiao, Zhu, Dustdar, Schahram
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-10-2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Data models; Destination prediction; Differential privacy; Location based services; neural network; Neural networks; Prediction algorithms; Predictive models; Privacy; privacy preservation; Recurrent neural networks; Servers; Tradeoffs; Trajectory; utility awareness
• ISSN: 1063-6692; 1558-2566
• DOI: 10.1109/TNET.2021.3084251

Destination prediction plays an important role as the basis for a variety of location-based services (LBSs). However, it poses many threats to users' location privacy. Most related work ignores privacy preservation in destination prediction. Few studies focus on specific kinds of privacy-preserving destination prediction algorithms and thus are not applicable to other prediction methods. Furthermore, the third party involved in these studies is a potential privacy threat. Additionally, another line of related work regarding LBSs neither guarantees the utility of the predicted results nor provides quantifiable privacy preservation. To this end, in this paper, we propose a general framework that can provide quantifiable privacy preservation and obtain a trade-off between the privacy and the utility of the predicted results by utilizing differential privacy and a neural network model. Specifically, it first adopts a specially designed differential privacy to construct a data-driven privacy-preserving model that formulates the relationship between injected noise and privacy preservation. Then, it combines a Recurrent Neural Network and Multi-hill Climbing to add fine-grained noise to obtain the trade-off between the privacy preservation and the utility of the predicted results. Our extensive experiments on real-world datasets validate that the proposed framework can be applied to different prediction methods, provide quantifiable location privacy preservation, and guarantee the utility of the predicted results simultaneously.