差分隐私的隐私观与隐私目标

doi:10.11959/j.issn.2096-109x.2023071

Abstract

Abstract:

The study aimed to address the challenges in understanding the privacy goals of differential privacy by analyzing the privacy controversies surrounding it in various fields.It began with the example of data correlation and highlighted the differing perspectives among scholars regarding the targets of privacy protection.In cases where records in a dataset were correlated, adversaries can exploit this correlation to infer sensitive information about individuals, thereby sparking a debate on whether this violates privacy protection.To investigate the influence of privacy theories in the legal domain on defining privacy, two mainstream privacy theories in the computer field were examined.The first theory, limited access to personal information, focuses on preventing others from accessing an individual’s sensitive information.According to this theory, privacy mechanisms should aim to prevent adversaries from accessing a user’s actual information.In contrast, the second theory, control over personal information, emphasizes an individual’s right to communicate personal information to others.This theory suggests that the disclosure of personal information due to the relevance of others sharing data should not be considered a breach of privacy.Then the controversies of differential privacy were analyzed in the fields of computer science, social science, ethics and human-computer interaction due to their different understandings of privacy.By exploring the privacy concept of differential privacy from a multidisciplinary perspective, this study helps readers gain a correct understanding of the privacy viewpoint and goals of differential privacy while enhancing their understanding of the concept of “privacy” itself.

Key words: differential privacy, data correlation, privacy risk, privacy theory

CLC Number:

TP3-05

Jingyu JIA, Chang TAN, Zhewei LIU, Xinhao LI, Zheli LIU, Tao ZHANG. Privacy view and target of differential privacy[J]. Chinese Journal of Network and Information Security, 2023, 9(5): 82-91.

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References 41

[1]	PRICE W N , COHEN I G . Privacy in the age of medical big data[J]. Nature Medicine, 2019,25(1): 37-43.
[2]	WANG Z , SONG M , ZHANG Z ,et al. Beyond inferring class representatives:User-level privacy leakage from federated learning[C]// IEEE INFOCOM 2019-IEEE Conference on Computer Communications. 2019: 2512-2520.
[3]	AYABURI E W , TREKU D N . Effect of penitence on social media trust and privacy concerns:The case of Facebook[J]. International Journal of Information Management, 2020,50: 171-181.
[4]	DWORK C , MCSHERRY F , NISSIM K ,et al. Calibrating noise to sensitivity in private data analysis[C]// Theory of Cryptography Conference. 2006: 265-284.
[5]	DWORK C , ROTH A . The algorithmic foundations of differential privacy[J]. Found Trends Theor Comput SCI, 2014,9(3-4): 211-407.
[6]	ERLINGSSON ú , PIHUR V , KOROLOVA A . Rappor:randomized aggregatable privacy-preserving ordinal response[C]// Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security. 2014: 1054-1067.
[7]	BITTAU A , ERLINGSSON ú , MANIATIS P ,et al. Prochlo:strong privacy for analytics in the crowd[C]// Proceedings of the 26th Symposium on Operating Systems Principles. 2017: 441-459.
[8]	Differential Privacy Team,Apple. Learning with privacy at scale[S]. 2017.
[9]	DING B , KULKARNI J , YEKHANIN S . Collecting telemetry data privately[C]// Proceedings of the 31st International Conference on Neural Information Processing Systems. 2017: 3574-3583.
[10]	JOHNSON N , NEAR J P , SONG D . Towards practical differential privacy for SQL queries[J]. Proceedings of the VLDB Endowment, 2018,11(5): 526-539.
[11]	NISSIM K , WOOD A . Is privacy privacy[J]. Philosophical Transactions of the Royal Society A:Mathematical,Physical and Engineering Sciences, 2018,376(2128): 20170358.
[12]	KIFER D , MACHANAVAJJHALA A . No free lunch in data privacy[C]// Proceedings of the 2011 ACM SIGMOD International Conference on Management of Data. 2011: 193-204.
[13]	COHEN A , NISSIM K . Towards formalizing the GDPR’s notion of singling out[J]. Proceedings of the National Academy of Sciences, 2020,117(15): 8344-8352.
[14]	OBERSKI D L , KREUTER F . Differential privacy and social science:an urgent puzzle[J]. Harvard Data Science Review:HDSR, 2020,2(1): 1-21.
[15]	ROGAWAY P . The moral character of cryptographic work[J]. Cryptology ePrint Archive, 2015.
[16]	CUMMINGS R , KAPTCHUK G , REDMILES E M . I need a better description:an investigation into user expectations for differential privacy[C]// Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security. 2021: 3037-3052.
[17]	HOLZEL J . Differential privacy and the GDPR[J]. Eur Data Prot L Rev, 2019,5:184.
[18]	XIONG A , WU C , WANG T ,et al. Using illustrations to communicate differential privacy trust models:an investigation of users' comprehension,perception,and data sharing decision[J]. arXiv Preprint arXiv:2202.10014, 2022.
[19]	XIONG A , WANG T , LI N ,et al. Towards effective differential privacy communication for users’ data sharing decision and comprehension[C]// 2020 IEEE Symposium on Security and Privacy (SP). 2020: 392-410.
[20]	KIFER D , MACHANAVAJJHALA A . Pufferfish:a framework for mathematical privacy definitions[J]. ACM Transactions on Database Systems (TODS), 2014,39(1): 1-36.
[21]	MICHAEL C T , SEN S , DATTA A . SoK:differential privacy as a causal property[C]// 2020 IEEE Symposium on Security and Privacy (SP). 2020: 354-371.
[22]	LIU C , CHAKRABORTY S , MITTAL P . Dependence makes you vulnberable:differential privacy under dependent tuples[C]// NDSS. 2016: 21-24.
[23]	ZHAO J , ZHANG J , POOR H V . Dependent differential privacy for correlated data[C]// 2017 IEEE Globecom Workshops (GC Wkshps). 2017: 1-7.
[24]	ZHU T , XIONG P , LI G ,et al. Correlated differential privacy:Hiding information in non-IID data set[J]. IEEE Transactions on Information Forensics and Security, 2014,10(2): 229-242.
[25]	YANG B , SATO I , NAKAGAWA H . Bayesian differential privacy on correlated data[C]// Proceedings of the 2015 ACM SIGMOD international conference on Management of Data. 2015: 747-762.
[26]	ZHANG T , ZHU T , LIU R ,et al. Correlated data in differential privacy:definition and analysis[J]. Concurrency and Computation:Practice and Experience, 2020.
[27]	LI Y , REN X , YANG S ,et al. Impact of prior knowledge and data correlation on privacy leakage:a unified analysis[J]. IEEE Transactions on Information Forensics and Security, 2019,14(9): 2342-2357.
[28]	CHEN R , FUNG B , YU P S ,et al. Correlated network data publication via differential privacy[J]. The VLDB Journal, 2014,23(4): 653-676.
[29]	SWEENEY L . k-anonymity:a model for protecting privacy[J]. International Journal of Uncertainty,Fuzziness and Knowledge-Based Systems, 2002,10(5): 557-570.
[30]	DALENIUS T . Towards a methodology for statistical disclosure control[J]. Statistik Tidskrift, 1977,15(429-444).
[31]	DWORK C , NAOR M . On the difficulties of disclosure prevention in statistical databases or the case for differential privacy[J]. Journal of Privacy and Confidentiality, 2010,2(1).
[32]	HIMMA K E , TAVANI H T . The handbook of information and computer ethics[M]. Hoboken: John Wiley ＆ Sons, 2008.
[33]	GAVISON R . Privacy and the limits of law[J]. The Yale Law Journal, 1980,89(3): 421-471.
[34]	BOK S . Secrets:on the ethics of concealment and revelation[M]. New York: Vintage, 1989.
[35]	TAVANI H T , MOOR J H . Privacy protection,control of information,and privacy-enhancing technologies[J]. ACM Sigcas Computers and Society, 2001,31(1): 6-11.
[36]	WESTIN A F . Privacy and freedom[J]. Washington and Lee Law Review, 1968,25(1): 166.
[37]	FRIED C . Privacy:a rational context[M]. New York:Computers,Ethics ＆ Society. 1990: 51-63.
[38]	BERKMAN B A . The assault on privacy:computers,data banks,and dossiers[J]. Case Western Reserve Law Review, 1971,22(4): 808.
[39]	RACHELS J . Why privacy is important[J]. Philosophy ＆ Public Affairs, 1975: 323-333.
[40]	STAFF F T C . Protecting consumer privacy in an era of rapid change-a proposed framework for businesses and policymakers[J]. Journal of Privacy and Confidentiality, 2011,3(1).
[41]	ABOWD J M . The US census Bureau adopts differential privacy[C]// Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery ＆ Data Mining. 2018: 2867-2867.

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