Generalization-based privacy preservation and discrimination prevention in data publishing and mining

Data Mining and Knowledge Discovery

Volumn 28, Issue 5-6, 2014, Pages 1158-1188

  Hajian, Sara ^a   Domingo Ferrer, Josep ^a   Farràs, Oriol ^a  

^a UNIVERSITAT ROVIRA I VIRGILI   (Spain)

Author keywords

Anti discrimination; Data mining; Generalization; Privacy

Indexed keywords

CLASSIFICATION (OF INFORMATION); DATA PRIVACY;

ANTI-DISCRIMINATION; GENERALIZATION; INFORMATION SOCIETY; OPTIMAL ALGORITHM; PRIVACY INVASIONS; PRIVACY PRESERVATION; PRIVACY PRESERVING; RULE INDUCTION ALGORITHMS;

DATA MINING;

EID: 84906788791     PISSN: 13845810     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10618-014-0346-1     Document Type: Article

References (48)

1. Aggarwal CC, Yu PS (eds) (2008) Privacy preserving data mining: models and algorithms. Springer, Berlin
2. Agrawal R, Srikant R (1994) Fast algorithms for mining association rules in large databases. In: Proceedings of the 20th international conference on very large data bases, VLDB, pp 487-499
3. Agrawal R, Srikant R (2000) Privacy preserving data mining. In: ACM SIGMOD 2000, pp 439-450
4. Australian Legislation (2008) (a) Equal Opportunity Act - Victoria State, (b) Anti-Discrimination Act - Queensland State
5. Bache K, Lichman M (2013) UCI machine learning repository. University of California, School of Information and Computer Science, Irvine, CA. http://archive.ics.uci.edu/ml. Accessed 20 Jan 2014
6. Bayardo RJ, Agrawal R (2005) Data privacy through optimal k-anonymization. In: ICDE 2005: IEEE, pp 217-228
7. Berendt B, Preibusch S (2012) Exploring discrimination: a user-centric evaluation of discrimination-aware data mining. In: IEEE 12th international conference on data mining workshops-ICDMW 2012, IEEE Computer Society, pp 344-351
8. Calders T, Verwer S (2010) Three naive Bayes approaches for discrimination-free classification. Data Mining Knowl Discov 21(2):277-292
9. Custers B, Calders T, Schermer B, Zarsky TZ (eds) (2013) Discrimination and privacy in the information society - data mining and profiling in large databases. Studies in applied philosophy, epistemology and rational ethics, vol 3. Springer, Berlin
10. Domingo-Ferrer J, Torra V (2005) Ordinal, continuous and heterogeneous k -anonymity through microaggregation. Data Mining Knowl Discov 11(2):195-212
11. Dwork C (2006) Differential privacy. In: ICALP 2006, LNCS 4052, Springer, pp 112
12. Dwork C (2011) A firm foundation for private data analysis. Commun ACM 54(1):8695
13. Dwork C, Hardt M, Pitassi T, Reingold O, Zemel RS (2012) Fairness through awareness. In: ITCS 2012, ACM, pp 214-226
14. European Union Legislation (1995) Directive 95/46/EC
15. European Union Legislation (2009) (a) Race Equality Directive, 2000/43/EC, 2000; (b) Employment Equality Directive, 2000/78/EC, 2000; (c) Equal Treatment of Persons, European Parliament legislative resolution, P6\-TA(2009) 0211
16. Fung BCM, Wang K, Yu PS (2005) Top-down specialization for information and privacy preservation. In: ICDE 2005, IEEE, pp 205-216
17. Fung BCM, Wang K, Fu AW-C, Yu P (2010) Introduction to privacy-preserving data publishing: concepts and techniques. Chapman & Hall/CRC, New York
18. Hajian S, Domingo-Ferrer J, Martínez-Ballesté A (2011) Rule protection for indirect discrimination prevention in data mining. In: MDAI 2011, LNCS 6820, Springer, pp 211-222
19. Hajian S, Domingo-Ferrer J (2013) A methodology for direct and indirect discrimination prevention in data mining. IEEE Trans Knowl Data Eng 25(7):1445-1459
20. Hajian S, Monreale A, Pedreschi D, Domingo-Ferrer J, Giannotti F (2012) Injecting discrimination and privacy awareness into pattern discovery. In: IEEE 12th international conference on data mining workshops-ICDMW 2012, IEEE Computer Society, pp 360-369
21. Hajian S, Domingo-Ferrer J (2012) A study on the impact of data anonymization on anti-discrimination. In: 2012 IEEE 12th international conference on data mining workshops-ICDMW 2012, IEEE Computer Society, pp 352-359
22. Hundepool A, Domingo-Ferrer J, Franconi L, Giessing S, Schulte-Nordholt E, Spicer K, de Wolf P-P (2012) Statistical disclosure control. Wiley, Chichester
23. Iyengar VS (2002) Transforming data to satisfy privacy constraints. In: SIGKDD 2002, ACM, pp 279288
24. Kamiran F, Calders T (2011) Data preprocessing techniques for classification without discrimination. Knowl Inf Syst 33(1):1-33
25. Kamiran F, Calders T, Pechenizkiy M (2010) Discrimination aware decision tree learning. In: ICDM 2010, IEEE, pp 869-874
26. Kamishima T, Akaho S, Asoh H, Sakuma J (2012) Fairness-aware classifier with prejudice remover regularizer. In: ECML/PKDD, LNCS 7524, Springer, pp 35-50
27. Lefevre K, Dewitt DJ, Ramakrishnan R (2005) Incognito: efficient full-domain k-anonymity. In SIGMOD 2005, ACM, pp 49-60
28. Lefevre K, Dewitt DJ, Ramakrishnan R (2006) Mondrian multidimensional k-anonymity. In: ICDE 2006, IEEE, p 25
29. Li N, Li T, Venkatasubramanian S (2007) t -Closeness: privacy beyond k -anonymity and l -diversity. In: IEEE ICDE 2007, IEEE, pp 106-115
30. Lindell Y, Pinkas B (2000) Privacy preserving data mining. In: Bellare M (ed) Advances in cryptology-CRYPTO'00, LNCS 1880, Springer, Berlin, pp 36-53
31. Loung BL, Ruggieri S, Turini F (2011) k-NN as an implementation of situation testing for discrimination discovery and prevention. In: KDD 2011, ACM, pp 502-510
32. Machanavajjhala A, Kifer D, Gehrke J, Venkitasubramaniam M (2007) l -Diversity: privacy beyond k -anonymity. ACM Trans Knowl Discov Data (TKDD) 1(1):Article 3
33. Mohammed N, Chen R, Fung BCM, Yu PS (2011) Differentially private data release for data mining. In: KDD 2011, ACM, pp 493-501
34. Pedreschi D, Ruggieri S, Turini F (2008) Discrimination-aware data mining. In: KDD 2008, ACM, pp 560-568
35. Pedreschi D, Ruggieri S, Turini F (2009) Measuring discrimination in socially-sensitive decision records. In: SDM 2009, SIAM, pp 581-592
36. Pedreschi D, Ruggieri S, Turini F (2009) Integrating induction and deduction for finding evidence of discrimination. In: ICAIL 2009, ACM, pp 157-166
37. Pedreschi D, Ruggieri S, Turini F (2013) The discovery of discrimination. In: Custers BHM, Calders T, Schermer BW, Zarsky TZ (eds) Discrimination and privacy in the information society: studies in applied philosophy, epistemology and rational, ethics. Springer, Berlin, pp 91-108
38. Ruggieri S, Pedreschi D, Turini F (2010) Data mining for discrimination discovery. ACM Trans Knowl Discov Data (TKDD) 4(2):Article 9
39. Samarati P (2001) Protecting respondents' identities in microdata release. IEEE Trans Knowl Data Eng 13(6):1010-1027
40. Samarati P, Sweeney L (1998) Generalizing data to provide anonymity when disclosing information. In: Proceedings of the 17th ACM SIGACTSIGMOD-SIGART symposium on principles of database systems (PODS 98), Seattle, WA, p 188
41. Statistics Sweden (2001) Statistisk rjandekontroll av tabeller, databaser och kartor (Statistical disclosure control of tables, databases and maps, in Swedish). Statistics Sweden, Örebro. http://www.scb.se/statistik/- publikationer/OV9999-2000I02-BR-X97P0102. Accessed 20 Jan 2014
42. Sweeney L (1998) Datafly: a system for providing anonymity in medical data. In: Proceedings of the IFIP TC11 WG11.3 11th international conference on database security XI: status and prospects, pp 356-381
43. Sweeney L (2002) k-Anonymity: a model for protecting privacy. Int J Uncertain Fuzziness Knowl Based Syst 10(5):557-570
44. United States Congress (1963) US Equal Pay Act (EPA) (Pub. L. 88-38). http://www.eeoc.gov/eeoc/history/35th/thelaw/epa.html. Accessed 20 Jan 2014
45. Wang K, Yu PS, Chakraborty S (2004) Bottom-up generalization: a data mining solution to privacy protection. In: ICDM 2004, IEEE, pp 249-256
46. Willenborg L, de Waal T (1996) Elements of statistical disclosure control. Springer, Berlin
47. Witten I, Frank E (2005) Data mining: practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco
48. Zliobaite I, Kamiran F, Calders T (2011) Handling conditional discrimination. In: ICDM 2011, IEEE, pp 992-1001