Abstract
The continuous release of location statistics plays a significant role in various real-world applications, such as traffic management and customization of public services. However, existing literature primarily focuses on static scenarios or perturbing locations at a single timestamp, disregarding the consideration of temporal correlation in mobile users. This oversight leaves the data susceptible to privacy attacks, including inference attacks, resulting in extra privacy leakage. To address this challenge, we propose a Local Differential Privacy Budget Distribution and Streaming Data Releasing (LPBD) mechanism for real-world location datasets. Specifically, we investigate the problem of continuously releasing location statistics for infinite streams while protecting user privacy and quantify the impact of temporal correlation on privacy leakage. The LPBD is a novel w-event level privacy-preserving mechanism, which has the capability to provide an adequate privacy budget for each timestamp and effectively mitigate the privacy leakage problem resulting from temporal correlation. Experimental results demonstrate that LPBD enhances data availability with strong privacy guarantees compared to state-of-the-art baseline methods.
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Data Availability
The datasets used to support the findings of this study are available from the corresponding author upon request.
References
Zhang X, Hamm J, Reiter MK, Zhang Y (2019) Statistical privacy for streaming traffic. In: Proceedings of the 26th ISOC Symposium on network and distributed system security
Wang Q, Zhang Y, Lu X, Wang Z, Qin Z, Ren K (2016) Real-time and spatio-temporal crowd-sourced social network data publishing with differential privacy. IEEE Trans Dependable Secure Comput 15(4):591–606
Wang T, Hu Z (2021) Real-time stream statistics via local differential privacy in mobile crowdsensing. In: Mobile multimedia communications: 14th EAI international conference, mobimedia 2021, virtual event, proceedings. Springer, pp 432–445
Cunningham T, Cormode G, Ferhatosmanoglu H, Srivastava D (2021) Real-world trajectory sharing with local differential privacy. arXiv preprint arXiv:2108.02084
Cao Y, Yoshikawa M, Xiao Y, Xiong L (2018) Quantifying differential privacy in continuous data release under temporal correlations. IEEE Trans Knowl Data Eng 31(7):1281–1295
Cao Y, Xiong L, Yoshikawa M, Xiao Y, Zhang S (2018) Contpl: controlling temporal privacy leakage in differentially private continuous data release. In: Proceedings of the VLDB endowment. International conference on very large data bases, vol 11. NIH Public Access, p 2090
Cao X, Cao Y, Yoshikawa M, Nakamura A (2022) Boosting utility of differentially private streaming data release under temporal correlations. In: 2022 IEEE International conference on big data (big data). IEEE, pp 6605–6607
Dwork C, Naor M, Pitassi T, Rothblum GN (2010) Differential privacy under continual observation. In: Proceedings of the forty-second ACM symposium on theory of computing, pp 715–724
Xiong X, Liu S, Li D, Cai Z, Niu X (2020) Real-time and private spatio-temporal data aggregation with local differential privacy. Journal of Information Security and Applications 55:102633
Wang H, Hong H, Xiong L, Qin Z, Hong Y (2022) L-srr: local differential privacy for location-based services with staircase randomized response. In: Proceedings of the 2022 ACM SIGSAC Conference on computer and communications security, pp 2809–2823
Erlingsson Ú, Pihur V, Korolova A (2014) Rappor: randomized aggregatable privacy-preserving ordinal response. In: Proceedings of the 2014 ACM SIGSAC Conference on computer and communications security, pp 1054–1067
Chen Y, Machanavajjhala A, Hay M, Miklau G (2017) Pegasus: data-adaptive differentially private stream processing. In: Proceedings of the 2017 ACM SIGSAC Conference on computer and communications security, pp 1375–1388
Fan L, Xiong L (2012) Real-time aggregate monitoring with differential privacy. In: Proceedings of the 21st ACM International conference on information and knowledge management, pp 2169–2173
Kellaris G, Papadopoulos S, Xiao X, Papadias D (2014) Differentially private event sequences over infinite streams. Proceedings of the VLDB endowment 7(12):1155–1166
Wang Z, Liu W, Pang X, Ren J, Liu Z, Chen Y (2020) Towards pattern-aware privacy-preserving real-time data collection. In: IEEE INFOCOM 2020-IEEE Conference on computer communications. IEEE, pp 109–118
Ren X, Shi L, Yu W, Yang S, Zhao C, Xu Z (2022) Ldp-ids: local differential privacy for infinite data streams. In: Proceedings of the 2022 International conference on management of data, pp 1064–1077
He Y, Wang F, Deng X, Ni J, Feng J, Liu S (2022) Ordinal data stream collection with condensed local differential privacy. In: 2022 IEEE 24th Int conf on high performance computing & communications; 8th Int conf on data science & systems; 20th Int conf on smart city; 8th Int conf on dependability in sensor, cloud & big data systems & application (HPCC/DSS/SmartCity/DependSys). IEEE, pp 562–569
Errounda FZ, Liu Y (2021) Collective location statistics release with local differential privacy. Futur Gener Comput Syst 124:174–186
Errounda FZ, Liu Y (2018) Continuous location statistics sharing algorithm with local differential privacy. In: 2018 IEEE International conference on big data (big data), pp 5147–5152
Xiao Y, Xiong L (2015) Protecting locations with differential privacy under temporal correlations. In: Proceedings of the 22nd ACM SIGSAC conference on computer and communications security, pp 1298–1309
Fang R, Han J, Yu J, Yao X, Peng H, Lu J (2021) Differentially private location preservation with staircase mechanism under temporal correlations. In: Proceedings of the 16th EAI International conference. Springer, pp 75–92
Chan T-HH, Shi E, Song D (2011) Private and continual release of statistics. ACM Transactions on Information and System Security (TISSEC) 14(3):1–24
Fan L, Xiong L (2013) An adaptive approach to real-time aggregate monitoring with differential privacy. IEEE Trans Knowl Data Eng 26(9):2094–2106
Wang Q, Zhang Y, Lu X, Wang Z, Qin Z, Ren K (2016) Rescuedp: real-time spatio-temporal crowd-sourced data publishing with differential privacy. In: IEEE INFOCOM 2016-The 35th Annual IEEE International conference on computer communications. IEEE, pp 1–9
Joseph M, Roth A, Ullman J, Waggoner B (2018) Local differential privacy for evolving data. Advances in Neural Information Processing Systems 31
Hemkumar D, Ravichandra S, Somayajulu DV (2021) Impact of data correlation on privacy budget allocation in continuous publication of location statistics. Peer-to-Peer Networking and Applications 14:1650–1665
Dwork C, Roth A et al (2014) The algorithmic foundations of differential privacy. Foundations and Trends® in Theoretical Computer Science 9(3–4):211–407
Li Y, Ren X, Yang S, Yang X (2019) Impact of prior knowledge and data correlation on privacy leakage: a unified analysis. IEEE Trans Inf Forensics Secur 14(9):2342–2357
Chen J, Ma H, Zhao D, Liu L (2017) Correlated differential privacy protection for mobile crowdsensing. IEEE Transactions on Big Data 7(4):784–795
Rafiei M, Elkoumy G, Aalst WM (2022) Quantifying temporal privacy leakage in continuous event data publishing. In: Cooperative information systems: 28th International conference, CoopIS 2022, proceedings. Springer, pp 75–94
Yuan J, Zheng Y, Xie X, Sun G (2011) T-drive: enhancing driving directions with taxi drivers’ intelligence. IEEE Trans Knowl Data Eng 25(1):220–232
Schäler C, Hütter T, Schäler M (2022) Benchmarking the utility of w-event differential privacy mechanisms–when baselines become mighty competitors
Cocchia A (2014) Smart and digital city: a systematic literature review. Smart city: how to create public and economic value with high technology in urban space, 13–43
Acknowledgements
This work was supported by the National Natural Science Foundation of China (No. 61702321)
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Hu, R., Li, H., Li, J. et al. Continuous release of temporal correlation location statistics with local differential privacy. Multimed Tools Appl 83, 50225–50243 (2024). https://doi.org/10.1007/s11042-023-17464-6
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DOI: https://doi.org/10.1007/s11042-023-17464-6