人工智能时代的数据隐私、垄断与公平

doi:10.11959/j.issn.2096-0271.2020004

摘要/Abstract

摘要：

随着人工智能时代的到来，大数据中蕴含的价值被不断开发，但与此同时，用户的隐私泄露问题、数据垄断问题以及算法决策中的公平问题愈发凸显。为详细探究此类伦理问题，首先从数据发展的角度出发，探讨人工智能时代隐私、垄断与公平问题的产生环境及其独特性。而后，对这3个伦理问题逐一分析其现状及挑战，得出当前伦理问题产生的本质是数据获取、使用以及决策的不透明性，提出建立数据透明机制是解决这些问题的重要举措。

关键词: 大数据, 数据伦理, 人工智能, 数据隐私, 数据垄断, 决策公平, 数据透明

Abstract:

With the coming of the era of artificial intelligence,the value contained in big data has been deeply mined.But at the same time,the privacy and data monopoly issues of users’ sensitive data,and fairness in algorithmic decisions have become increasingly serious.In order to explore such problems,firstly,the development of data was researched,which reflects the unique producing environment of data ethics in the era of artificial intelligence,and the unique properties of these ethical issues were discussed.Then,the data monopoly,privacy disclosure and unfair decision-making were discussed one by one,whose development status and challenges were analyzed.It is concluded that the essence of current ethical issues is the non-transparency of data collection,data usage and algorithm decision,so that establishing the data transparency mechanism should be an important measure to solve these problems.

Key words: big data, data ethics, artificial intelligence, data privacy, data monopoly, decision fairness, data transparency

中图分类号:

TP309

孟小峰, 王雷霞, 刘俊旭. 人工智能时代的数据隐私、垄断与公平[J]. 大数据, 2020, 6(1): 35-46.

Xiaofeng MENG, Leixia WANG, Junxu LIU. Data privacy,monopoly and fairness for AI[J]. Big Data Research, 2020, 6(1): 35-46.

图/表 2

参考文献 44

[1]	STOYANOVICH J , HOWE B , JAGADISH H V ,et al. Panel:a debate on data and algorithmic ethics[J]. Very Large Data Bases, 2018,11(12): 2165-2167.
[2]	STOYANOVICH J , ABITEBOUL S , MIKLAU G ,et al. Data,responsibly:fairness,neutrality and transparency in data analysis[C]// International Conference on Extending Database Technology,March 15-18,2016,Bordeaux,France. Heidelberg:Springer, 2016: 718-719.
[3]	GANTZ J , REINSEL D . The digital universe in 2020:big data,bigger digital shadows,and biggest growth in the far east[J]. IDC iView:IDC Analyze the Future, 2012(1): 1-16.
[4]	BORGMAN C L . 大数据、小数据、无数据：网络世界的数据学术[M]. 孟小峰,等,译.北京: 机械工业出版社, 2017.
	BORGMAN C L . Big data,little data,no data:scholarship in the networked world[M]. Translated by MENG X F,et al. Beijing: China Machine PressPress, 2017.
[5]	方滨兴, 贾焰, 李爱平 ,等. 大数据隐私保护技术综述[J]. 大数据, 2016,2(1): 1-18.
	FANG B X , JIA Y , LI A P ,et al. Privacy preserving in big data:a survey[J]. Big Data Research, 2016,2(1): 1-18.
[6]	孟小峰, 张啸剑 . 大数据隐私管理[J]. 计算机研究与发展, 2015,52(2): 265-281.
	MENG X F , ZHANG X J . Big data management[J]. Journal of Computer Research and Development, 2015,52(2): 265-281.
[7]	张啸剑, 孟小峰 . 面向数据发布和分析的差分隐私保护[J]. 计算机学报, 2014,37(4): 927-949.
	ZHANG X J , MENG X F . Differential privacy in data publication and analysis[J]. Chinese Journal of Computers, 2014,37(4): 927-949.
[8]	叶青青, 孟小峰, 朱敏杰 ,等. 本地化差分隐私研究综述[J]. 软件学报, 2018,29(7): 159-183.
	YE Q Q , MENG X F , ZHU M J ,et al. Survey on local differential privacy[J]. Journal of Software, 2018,29(7): 159-183.
[9]	刘俊旭, 孟小峰 . 机器学习的隐私保护研究综述[J]. 计算机研究与发展, 2019.已录用
	LIU J X , MENG X F . Survey on privacypreserving machine learning[J]. Journal of Computer Research and Development, 2019,Accepted.
[10]	SWEENEY L . k-anonymity:a model for protecting privacy[J]. International Journal of Uncertainty,Fuzziness and Knowledge-Based Systems, 2002,10(5): 557-570.
[11]	LI N H , LI T C , VENKATASUBRAMANIAN S ,et al. Closeness:a new privacy measure for data publishing[J]. IEEE Transactions on Knowledge and Data Engineering, 2010,22(7): 943-956.
[12]	XIAO X K , TAO Y F . M-invariance:towards privacy preserving republication of dynamic datasets[C]// The 2007 ACM SIGMOD International Conference on Management of Data,June 11-14,2007,Beijing,China. New York:ACM Press, 2007: 689-700.
[13]	BU Y Y , FU A W C , WONG R C W ,et al. Privacy preserving serial data publishing by role composition[J]. Proceedings of the VLDB Endowment, 2008,1(1): 845-856.
[14]	LI C , PALANISAMY B . ReverseCloak:protecting multi-level location privacy over road networks[C]// The 24th ACM International Conference on Information and Knowledge Management,October 18-23,2015,Melbourne,Australia. New York:ACM Press, 2015: 673-682.
[15]	CHENG J , FU A C W , LIU J . K-Isomorphism:privacy preserving network publication against structural attacks[C]// The 2010 ACM SIGMOD International Conference on Management of Data,June 6-10,2010,Indianapolis,USA. New York:ACM Press, 2010: 459-470.
	ZHAO C , ZOU L , LI F F . Privacy preserving subgraph matching on large graphs in cloud[C]// The 2016 International Conference on Management of Data,June 26-July 1,2016,San Francisco,USA. New York:ACM Press, 2016: 199-213.
[17]	DWORK C . Differential privacy[J]. Encyclopedia of Cryptography and Security, 2006(1): 1-12.
[18]	QARDAJI W , YANG W N , LI N H . PriView:practical differentially private release of marginal contingency tables[C]// International Conference on Management of Data,June 22-27,2014,Snowbird,USA. New York:ACM Press, 2014: 1435-1446.
[19]	DAY W Y , LI N H , LYU M . Publishing graph degree distribution with node differential privacy[C]// The 2016 International Conference on Management of Data,June 26-July 1,2016,San Francisco,USA. New York:ACM Press, 2016: 123-138.
[20]	XU S Z , SU S , XIONG L ,et al. Differentially private frequent subgraph mining[C]// 2016 IEEE 32nd International Conference on Data Engineering,May 16-20,2016,Helsinki,Finland. Piscataway:IEEE Press, 2016: 229-240.
[21]	ZHANG J , XIAO X K , XIE X . PrivTree:a differentially private algorithm for hierarchical decompositions[C]// The 2016 International Conference on Management of Data,June 26-July 1,2016,San Francisco,USA. New York:ACM Press, 2016: 155-170.
[22]	KRISHNAN S , WANG J N , FRANKLIN M J ,et al. PrivateClean:data cleaning and differential privacy[C]// The 2016 International Conference on Management of Data,June 26-July 1,2016,San Francisco,USA. New York:ACM Pres, 2016: 937-951.
[23]	WARNER S L . Randomized response:a survey technique for eliminating evasive answer bias[J]. Journal of the American Statistical Association, 1965,60(309): 63-69.
[24]	ERLINGSSON ú , PIHUR V , KOROLOVA A . RAPPOR:randomized aggregatable privacy-preserving ordinal response[C]// The 2014 ACM SIGSA Conference on Computer and Communications Security,November 3-7,2014,Hong Kong,China. New York:ACM Press, 2014: 1054-1067.
[25]	BUN M , NELSON J , STEMMER U . Heavy hitters and the structure of local privacy[C]// The 37th ACM SIGMODSIGACT-SIGAI Symposium on Principles of Database Systems,June 10-15,2018,Houston,USA. New York:ACM Press, 2018: 435-447.
[26]	WANG T H , BLOCKI J , LI N H . Locally differentially private protocols for frequency estimation[C]// USENIX Security Symposium,August 16-18,2017,Vancouver,Canada. Berkeley:USENIX Association, 2017: 729-745.
[27]	WANG T H , LI N H , JHA S . Locally differentially private frequent itemset mining[C]// IEEE Symposium on Security and Privacy,May 21-23,2018,San Francisco,USA. Piscataway:IEEE Press, 2018: 127-143.
[28]	YE Q Q , HU H B , MENG X F ,et al. PrivKV:key-value data collection with local differential privacy[C]// IEEE Symposium on Security and Privacy,May 20-22,2019,San Francisco,USA. Piscataway:IEEE Press, 2019: 317-331.
[29]	QIN Z , YU T , YANG Y ,et al. Generating synthetic decentralized social graphs with local differential privacy[C]// 2018 IEEE 4th International Conference on Computer and Communications Security,June 22-24,2018,Haikou,China. New York:ACM Press, 2017: 425-438.
[30]	SUN H , XIAO X , KHALIL I ,et al. Analyzing subgraph statistics from extended local views with decentralized differential privacy[C]// 2019 IEEE 5th International Conference on Computer and Communications Security,November 11,2019,London,UK. New York:ACM Press, 2019: 703-717.
[31]	ZHANG Z K , WANG T H , LI N H ,et al. CALM:consistent adaptive local marginal for marginal release under local differential privacy[C]// 2018 IEEE 4th International Conference on Computer and Communications Security,June 22-24,2018,Haikou,China. New York:ACM Press, 2018: 212-229.
[32]	PAILLIER P , . Public-key cryptosystems based on composite degree residuosity classes[C]// International Conference on the Theory and Application of Cryptographic Techniques,May 2-6,1999,Prague,Czech Republic.[S.l.:s.n. ], 1999: 223-238.
[33]	STEHLé D , STEINFELD R , . Faster fully homomorphic encryption[C]// International Conference on the Theory and Application of Cryptology and Information Security,December 5-9,2010,Singapore. Heidelberg:Springer, 2010: 377-394.
[34]	DOMINGO-FERRER J , . A provably secure additive and multiplicative privacy homomorphism[C]// The 5th International Conference on Information Security,September 30-October 2,2002,S?o Paulo,Brazil. Heidelberg:Springer, 2002: 471-483.
[35]	PLANTARD T , SUSILO W , ZHANG Z F . Fully homomorphic encryption using ideal lattices[C]// International Conference on Theory of Computing,May 31-June 2,2009,Bethesda,USA. Heidelberg:Springer, 2009: 169-178.
[36]	HU H , XU J , XU X ,et al. Private search on key-value stores with hierarchical indexes[C]// 2014 IEEE 30th International Conference on Data Engineering,March 31-April 4,2014,Chicago,USA. Piscataway:IEEE Press, 2014: 628-639.
[37]	HU H B , XU J L , REN C S ,et al. Processing private queries over untrusted data cloud through privacy homomorphism[C]// 2011 IEEE 27th International Conference on Data Engineering,April 11-16,2011,Hannover,Germany. Piscataway:IEEE Press, 2011: 639-644.
[38]	DU W L , ATALLAH M J . Secure multi-party computation problems and their applications:a review and open problems[C]// The New Security Paradigms Workshop,September 10-13,New Mexico,USA. New York:ACM Press, 2001: 13-22.
[39]	VAIDYA J , CLIFTON C . Privacy preserving association rule mining in vertically partitioned data[C]// ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,July 23-26,2002,Edmonton,Canada. New York:ACM Press, 2002: 639-644.
[40]	SHEIKH R , MISHRA D K , KUMAR B . Secure multiparty computation:from millionaires problem to anonymizer[J]. Information Security Journal:A Global Perspective, 2009,20(1): 25-33.
[41]	孟小峰, 朱敏杰, 刘俊旭 . 大规模用户隐私风险量化研究[J]. 信息安全研究, 2019(9): 778-788.
	MENG X F , ZHU M J , LIU J X . Quatitative research on privacy risk of large-scale mobile user[J]. Journal of Information Security Research, 2019(9): 778-788.
[42]	孟小峰, 朱敏杰, 刘立新 ,等. 数据垄断与其治理模式研究[J]. 信息安全研究, 2019(9): 789-797.
	MENG X F , ZHU M J , LIU L X ,et al. Research on data monopoly and its governance modes[J]. Journal of Information Security Research, 2019(9): 789-797.
[43]	GUPTA M , . How do we make AI fair? (Keynote)[C]// International Conference on Systems and Machine Learning,December 15,2019,Boca Raton,USA.[S.l.:s.n]. 2019.
[44]	ZHANG X Y , WANG N F , JI S L ,et al. Interpretable deep learning under fire[C]// USENIX Security Symposium 2020,August 12-14,2020,Boston,USA.[S.l.:s.n]. 2020,accepted.