Preserving worker privacy in crowdsourcing

Data Mining and Knowledge Discovery

Volumn 28, Issue 5-6, 2014, Pages 1314-1335

  Kajino, Hiroshi ^a   Arai, Hiromi ^b   Kashima, Hisashi ^c  

^a UNIVERSITY OF TOKYO   (Japan)

^b RIKEN   (Japan)

^c KYOTO UNIVERSITY   (Japan)

Author keywords

Crowdsourcing; EM algorithm; Privacy preserving data mining; Quality control

Indexed keywords

ALGORITHMS; QUALITY CONTROL;

CROWDSOURCING; EM ALGORITHMS; LOW QUALITIES; PERSONAL INFORMATION; PRIVACY PRESERVING DATA MINING; PRIVACY PROBLEMS; RELIABLE RESULTS; SECURE COMPUTATION;

QUALITY ASSURANCE;

EID: 84906781536     PISSN: 13845810     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10618-014-0352-3     Document Type: Article

References (19)

1. Agrawal R, Srikant R (2000) Privacy-preserving data mining. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, pp 439-450
2. Bernstein M, Chi EH, Chilton L, Hartmann B, Kittur A, Miller RC (2011) Crowdsourcing and human computation: systems, studies and platforms. In: Proceedings of CHI 2011 Workshop on Crowdsourcing and Human Computation, pp 53-56
3. Burkhart M, Strasser M, Many D, Dimitropoulos X (2010) SEPIA: privacy-preserving aggregation of multi-domain network events and statistics. In: Proceedings of the 19th USENIX Conference on Security, pp 223-240
4. Damgård I, Jurik M (2001) A Generalisation, a simplification and some applications of Paillier's probabilistic public-key system. In: Proceedings of the 4th International Workshop on Practice and Theory in Public Key Cryptography: Public Key Cryptography, pp 119-136
5. Dawid AP, Skene AM (1979) Maximum likelihood estimation of observer error-rates using the EM algorithm. J R Stat Soc Ser C 28(1):20-28
6. Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc Ser B 39(1):1-38
7. Ertekin S, Hirsh H, Rudin C (2012) Learning to predict the wisdom of crowds. In: Proceedings of Collective Intelligence 2012
8. Lease M (2011) On quality control and machine learning in crowdsourcing. In: Proceedings of the Third Human Computation Workshop, pp 97-102
9. Lin X, Clifton C, Zhu M (2005) Privacy-preserving clustering with distributed EM mixture modeling. Knowl Inf Syst 8(1):68-81
10. Lindell Y, Pinkas B (2000) Privacy preserving data mining. In: Advances in Cryptology-CRYPTO '00, pp 36-54
11. Nabar SU, Kenthapadi K, Mishra N, Motwani R (2008) A survey of query auditing techniques for data privacy. In: Privacy-Preserving Data Mining: Models and Algorithms, pp 415-431
12. Raykar VC, Yu S, Zhao LH, Florin C, Bogoni L, Moy L (2010) Learning from crowds. J Mach Learn Res 11:1297-1322
13. Shamir A (1979) How to share a secret. Commun ACM 22(11):612-613. doi: 10.1145/359168.359176
14. Sheng VS, Provost F, Ipeirotis PG (2008) Get another label? Improving data quality and data mining using multiple, noisy labelers. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 614-622
15. Sweeney L (2002) k-anonymity: a model for protecting privacy. Int J Uncertain Fuzziness Knowl Syst 10(5):557-570. doi: 10.1142/S0218488502001648
16. Varshney LR (2012) Privacy and reliability in crowdsourcing service delivery. In: Proceedings of the 2012 Annual SRII Global Conference, pp 55-60
17. Welinder P, Branson S, Belongie S, Perona P (2010) The multidimensional wisdom of crowds. Adv Neural Inf Process Syst 23:2424-2432
18. Whitehill J, Ruvolo P, Wu T, Bergsma J, Movellan J (2009) Whose vote should count more: optimal integration of labels from labelers of unknown expertise. Adv Neural Inf Process Syst 22:2035-2043
19. Yang B, Sato I, Nakagawa H (2012) Privacy-preserving EM algorithm for clustering on social network. In: Advances in Knowledge Discovery and Data Mining 16th Pacific-Asia Conference, PAKDD 2012, pp 542-553