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Privacy protection in government data sharing: an improved LDP-based approach

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Abstract

Governments own various types and large amounts of individual data. One governmental department manages specific areas of data. To develop smart government, data need to be shared among the governmental departments. However, how to prevent potential attackers from getting private information in data sharing is a challenging problem. To protect private information while sharing statistics among government departments, an improved LDP-based (local differential privacy) approach is proposed. This approach combines the data binning technique with the count mean sketch (CMS) algorithm. Equi-width binning is adopted to divide the data records into smaller data domains to overcome the problem of large statistical errors in the current privacy protection algorithms with large data domain size and small amounts of data. Then, the proposed algorithm is compared with the CMS and HCMS algorithms from different aspects such as frequency estimation, data size, privacy budget, and data domain size. Experimental results show that the proposed algorithm effectively reduces statistical errors and enhances the utility of data after privacy protection with both various distributions and data domain sizes.

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References

  1. Acharya J, Sun Z, Zhang H (2018) Hadamard response: Estimating distributions privately, efficiently, and with little communication p 120.arXiv:1802.04705

  2. Alenezi H, Tarhini A, Sharma SK (2015) Development of quantitative model to investigate the strategic relationship between information quality and e-government benefits. Transf Gov People Process Policy 9(3):324–351. https://doi.org/10.1108/TG-01-2015-0004

    Article  Google Scholar 

  3. Bassily R, Smith A (2015) Local, private, efficient protocols for succinct histograms. In: STOC ’15: Proceedings of the forty-seventh annual ACM symposium on Theory of Computing (June):127–135, https://doi.org/10.1145/2746539.2746632.arXiv: 1911.12834?context=cs

  4. Bassily R, Nissim K, Stemmer U, Thakurta A (2017) Practical locally private heavy hitters. Adv Neural Inf Process Syst pp 2285–2293.arXiv:1707.04982v1

  5. Chatfield AT, Reddick CG (2019) A framework for internet of things-enabled smart government: a case of iot cybersecurity policies and use cases in U.S. federal government. Library Inf Service 36(2):346–357. https://doi.org/10.1016/j.giq.2018.09.007

  6. Christofides TC (2003) A generalized randomized response technique. Metrika 57(2):195–200. https://doi.org/10.1007/s001840200216

    Article  MathSciNet  MATH  Google Scholar 

  7. Cormode G, Muthukrishnan S (2004) An improved data stream summary: The count-min sketch and its applications. J. Algorithm 55(1):5875. https://doi.org/10.1007/978-3-540-24698-5-7

    Article  MathSciNet  MATH  Google Scholar 

  8. Differential Privacy Team A (2017) Learning with privacy at scale. Mach Learn J 1(8):125

    Google Scholar 

  9. Duchi JC, Jordan MI, Wainwright MJ (2008) Local privacy and statistical minimax rates. In: In Proceedings of the 2008 IEEE symposium on security and privacy (sp 2008), IEEE, pp 429–438. https://doi.org/10.1109/FOCS.2013.53

  10. Duchi JC, Jordan MI, Wainwright MJ (2014) Privacy aware learning. J ACM 61(6):1–57. https://doi.org/10.1145/2666468

    Article  MathSciNet  MATH  Google Scholar 

  11. Dwork C (2011) Differential privacy. In Encyclopedia of Cryptography and Security (2nd Ed) pp 338–340. https://doi.org/10.1007/11787006-1

  12. Dwork C (2012) Calibrating noise to sensitivity in private data analysis. Lect Notes Comput Sci 3876(8):265–284. https://doi.org/10.1007/11681878-14

    Article  MathSciNet  MATH  Google Scholar 

  13. Erlingsson L, Pihur V, Korolova A (2014) Rappor: randomized aggregatable privacy-preserving ordinal response. Proc ACM Conf Comput Commun Secur 61(6):10541067. https://doi.org/10.1145/2660267.2660348

    Article  Google Scholar 

  14. Fang X, Zeng Q, Yang G (2020) Local differential privacy for human-centered computing. EURASIP J Wirel Commun Netw 2020(65). https://doi.org/10.1186/s13638-020-01675-8

  15. Fanti G, Pihur V, Erlingsson L (2016) Building a rappor with the unknown: privacy-preserving learning of associations and data dictionaries. In: Proceedings on privacy enhancing technologies 2016(3):41–61.arXiv:1503.01214v1

  16. Gu X, Li M, Cheng Y, Xiong L, Cao Y (2019) Pckv: Locally differentially private correlated key-value data collection with optimized utility. Usenix Secur Symp 2020:1–18

    Google Scholar 

  17. Homer N, Szelinger S, Redman M, Duggan D, Tembe W, Muehling J, Pearson JV, Stephan DA, Nelson SF, Craig DW (2008) Resolving individuals contributing trace amounts of dna to highly complex mixtures using high-density snp genotyping microarrays. PLoS Genet 4(8):1–9. https://doi.org/10.1371/journal.pgen.1000167

    Article  Google Scholar 

  18. Joseph M, Roth A, Ullman J, Waggoner B (2018) Local differential privacy for evolving data. In: NIPS’18: Proceedings of the 32nd international conference on neural information processing systems p 23752384. https://doi.org/10.29012/jpc.718

  19. Lu R, Liang X, Li X, Lin X, Shen X (2012) Eppa: an efficient and privacy-preserving aggregation scheme for secure smart grid communications. IEEE Trans Parallel Distrib Syst 23(9):1621–1631. https://doi.org/10.1109/TPDS.2012.86

    Article  Google Scholar 

  20. Lv Z, Li X, Wang W, Zhang B, Hu J, Feng S (2017) Government affairs service platform for smart city. Future Gen Comput Syst 81:443–451. https://doi.org/10.1016/j.future.2017.08.047

    Article  Google Scholar 

  21. Marmol FG (2012) Do not snoop my habits: preserving privacy in the smart grid. Commun Mag IEEE 50(5):166–172. https://doi.org/10.1109/MCOM.2012.6194398

    Article  Google Scholar 

  22. Myttenaere AD, Golden B, Grand BL, Rossi F (2016) Mean absolute percentage error for regression models. Neurocomputing 192:38–48. https://doi.org/10.1016/j.neucom.2015.12.114

    Article  Google Scholar 

  23. Narayanan A, Shmatikov V (2008) Robust de-anonymization of large sparse datasets. In: In Proceedings of the 2008 IEEE symposium on security and privacy (sp 2008), IEEE, pp 111–125. https://doi.org/10.1109/SP.2008.33

  24. Omori Y, Yamashita T (2020) Extended inter-device digital rights sharing and transfer based on device-owner equality verification using homomorphic encryption. IEICE Trans Inf Syst E103D(6):1339–1354. https://doi.org/10.1587/transinf.2019EDP7163

    Article  Google Scholar 

  25. Perboli G, Marco AD, Perfetti F, Marone M (2014) A new taxonomy of smart city projects. Transp Res Procedia 3:470–478. https://doi.org/10.1016/j.trpro.2014.10.028

    Article  Google Scholar 

  26. Piao C, Shi Y, Yan J, Zhang C, Liu L (2018) Privacy-preserving governmental data publishing: a fog-computing-based differential privacy approach. Future Gen Comput Syst 90:159–174. https://doi.org/10.1016/j.future.2018.07.038

    Article  Google Scholar 

  27. Ruggles S, Fitch C, Magnuson D, Schroeder J (2019) Differential privacy and census data: implications for social and economic research. Aea Papers Proc 109(MAY):403–408. https://doi.org/10.1257/pandp.20191107

    Article  Google Scholar 

  28. Sei Y, Ohsuga A (2017) Differential private data collection and analysis based on randomized multiple dummies for untrusted mobile crowdsensing. IEEE Trans Inf Forensics Secur 12(4):926–939. https://doi.org/10.1109/TIFS.2016.2632069

    Article  Google Scholar 

  29. Sei Y, Okumura H, Takenouchi T, Ohsuga A (2019) Anonymization of sensitive quasi-identifiers for l-diversity and t-closeness. IEEE Trans Dependable Secure Comput 16(4):580–593. https://doi.org/10.1109/TDSC.2017.2698472

    Article  Google Scholar 

  30. Shang S, Du J (2019) Smart government function construction analysis and path design under big data. Inf Stud Theory Appl 42(04):45–51

    Google Scholar 

  31. Shen J, Liu D, Sun X, Wei F, Xiang Y (2020) Efficient cloud-aided verifiable secret sharing scheme with batch verification for smart cities. Future Gen Comput Syst 109:450–456. https://doi.org/10.1016/j.future.2018.10.049

    Article  Google Scholar 

  32. Susan VS, Christopher T (2016) Anatomisation with slicing: a new privacy preservation approach for multiple sensitive attributes. Springerplus 5(1):1–21. https://doi.org/10.1186/s40064-016-2490-0

    Article  Google Scholar 

  33. Sweeney L (2002) k-anonymity: A model for protecting privacy. Int J Uncert Fuzz Knowl Based Syst 10(05):557–570. https://doi.org/10.1142/S0218488502001648

    Article  MathSciNet  MATH  Google Scholar 

  34. Vu DH, Luong TD, Ho TB (2020) An efficient approach for secure multi-party computation without authenticated channel. Inf Sci 527(JULY):356–368. https://doi.org/10.1016/j.ins.2019.07.031

    Article  MathSciNet  MATH  Google Scholar 

  35. Wang T, Blocki J (2017) Locally differentially private protocols for frequency estimation. In: Proceedings of the 26th USENIX security symposium (AUG.):729745

  36. Wang T, Lopuha-Zwakenberg M, Li Z, Skoric B, Li N (2019) Consistent and accurate frequency oracles under local differential privacy. Netw Distrib Syst Securit Symp (NDSS) p 23752384.arXiv:1905.08320

  37. Warner SL (1965) Randomized response: a survey technique for eliminating evasive answer bias. J Am Stat Assoc 60(309):63–69. https://doi.org/10.1080/01621459.1965.10480775

    Article  MATH  Google Scholar 

  38. Wirtz BW, Weyerer JC, Schichte FT (2019) An integrative public iot framework for smart government. Govern Inf Quart 36(2):333–345. https://doi.org/10.1016/j.giq.2018.07.001

    Article  Google Scholar 

  39. Xu X, Liu Q, Zhang X, Zhang J, Qi L, Dou W (2019) A blockchain-powered crowdsourcing method with privacy preservation in mobile environment. IEEE Trans Comput Soc Syst 6(6):1407–1419. https://doi.org/10.1109/TCSS.2019.2909137

    Article  Google Scholar 

  40. Yan Z, Liu J, Liu S (2019) Dpwevote: differentially private weighted voting protocol for cloud-based decision-making. Enterp Inf Syst 13(2):1–21. https://doi.org/10.1080/17517575.2018.1442935

    Article  Google Scholar 

  41. Ye Q, Meng X, Zhu M, Huo Z (2018) A study of local differential privacy research. J Softw 29(07):1981–2005

    MathSciNet  Google Scholar 

  42. Ye Q, Hu H, Meng X, Zheng H (2019) Privkv: Key-value data collection with local differential privacy. In: 2019 IEEE symposium on security and privacy (SP). https://doi.org/10.1109/SP.2019.00018

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (71701091), the Chinese Ministry of Education Project of Humanities and Social Science(17YJC870020), and the Graduate Innovation Foundation of Hebei Province (CXZZSS2020071).

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Authors and Affiliations

  1. Shijiazhuang Tiedao University, Shijiazhuang, 050043, People’s Republic of China

    Chunhui Piao & Yurong Hao

  2. Nanjing University, Nanjing, 210023, People’s Republic of China

    Jiaqi Yan

  3. Construction Information Center of Hebei Province Department of Housing&Urban-Rural Development, Shijiazhuang, 050043, Hebei, People’s Republic of China

    Xuehong Jiang

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  1. Chunhui Piao
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Correspondence to Jiaqi Yan.

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Piao, C., Hao, Y., Yan, J. et al. Privacy protection in government data sharing: an improved LDP-based approach. SOCA 15, 309–322 (2021). https://doi.org/10.1007/s11761-021-00315-3

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