Discrimination- and privacy-aware patterns

Data Mining and Knowledge Discovery

Volumn 29, Issue 6, 2015, Pages 1733-1782

  Hajian, Sara ^a   Domingo Ferrer, Josep ^a   Monreale, Anna ^b   Pedreschi, Dino ^b   Giannotti, Fosca ^c  

^a UNIVERSITAT ROVIRA I VIRGILI   (Spain)

^b UNIVERSITY OF PISA   (Italy)

^c ISTI CNR   (Italy)

Author keywords

Anti discrimination; Data mining; Frequent patterns; Privacy

Indexed keywords

BEHAVIORAL RESEARCH; DATA MINING; DECISION MAKING; ECONOMIC AND SOCIAL EFFECTS; POPULATION STATISTICS;

ANTI-DISCRIMINATION; DECISION MAKING PROCESS; DIFFERENTIAL PRIVACIES; FREQUENT PATTERN MINING; FREQUENT PATTERNS; PATTERN DISTORTION; SENSING TECHNOLOGY; SENSITIVE INFORMATIONS;

DATA PRIVACY;

EID: 84942502827     PISSN: 13845810     EISSN: 1573756X     Source Type: Journal    
DOI: 10.1007/s10618-014-0393-7     Document Type: Article

References (56)

1. Aggarwal CC, Yu PS (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: SIGMOD 2000. ACM Press, New York, pp 439–450
4. Atzori M, Bonchi F, Giannotti F, Pedreschi D (2008) Anonymity preserving pattern discovery. VLDB J 17(4):703–727
5. Australian Legislation (2014) (a) Victorian Current Acts - Equal Opportunity Act - 2010 (amended Sept. 17, 2014); (b) Queensland - Anti-Discrimination Act 1991 (current as at July 1, 2014)
6. Berendt B, Preibusch S (2014) Better decision support through exploratory discrimination-aware data mining: foundations and empirical evidence. Artif Intell Law 22(2):175–209
7. Bhaskar R, Laxman S, Smith A, Thakurta A (2010) Discovering frequent patterns in sensitive data. In KDD 2010. ACM Press, New York, pp 503–512
8. Bonomi L (2013) Mining frequent patterns with differential privacy. PVLDB 6(12):1422–1427
9. Calders T, Goethals B (2007) Non-derivable itemset mining. DMKD 14(1):171–206
10. Calders T, Verwer S (2010) Three naive Bayes approaches for discrimination-free classification. Data Min. Knowl. Discov. 21(2):277–292
11. Custers B, Calders T, Schermer B, Zarsky TZ (eds) Discrimination and privacy in the information society—data mining and profiling in large databases. Studies in Applied Philosophy, Epistemology and Rational Ethics 3. Springer, Berlin (2013)
12. Dalenius T (1974) The invasion of privacy problem and statistics production—an overview. Statistik Tidskrift 12:213–225
13. Domingo-Ferrer J, Torra V (2005) Ordinal, continuous and heterogeneous k-anonymity through microaggregation. Data Min Knowl Discov 11(2):195–212
14. Dwork C (2006) Differential privacy. In: ICALP 2006 LNCS 4052. Springer, Berlin, pp 112
15. Dwork C, Hardt M, Pitassi T, Reingold O, Zemel RS (2012) Fairness through awareness. In: ITCS 2012. ACM Press, New York, pp 214–226
16. European Union Legislation (1995) Directive 95/46/EC
17. European Union Legislation (2014) (a) Racial Equality Directive, 2000/43/EC; (b) Employment Equality Directive, 2000/78/EC; (c) European Parliament legislative resolution on equal treatment between persons irrespective of religion or belief, disability, age or sexual orientation (A6-0149/2009)
18. Frank A, Asuncion A (2010) UCI machine learning repository. University of California, School of Information and Computer Science, Irvine http://archive.ics.uci.edu/ml/datasets
19. Friedman A, Wolff R, Schuster A (2008) Providing k-anonymity in data mining. VLDB J 17(4):789–804
20. Friedman A, Schuster A (2010) Data mining with differential privacy. In: KDD 2010. ACM, New York, pp 493–502
21. Fung BCM, Wang K, Fu AW-C, Yu PS (2010) Introduction to privacy-preserving data publishing: concepts and techniques. Chapman & Hall/CRC, Boca Raton
22. Gehrke J, Hay M, Lui E, Pass R (2012) Crowd-blending privacy. In: CRYPTO pp 479–496
23. Greenwood PE, Nikulin MS (1996) A guide to chi-squared testing. Wiley, New York
24. Hajian S, Domingo-Ferrer J, Martínez-Ballesté A (2011) Rule protection for indirect discrimination prevention in data mining. In: MDAI 2011 Lectuer Notes in Computer Science vol 6820. Springer, Berlin, pp 211–222
25. 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
26. Hajian S, Monreale A, Pedreschi D, Domingo-Ferrer J, Giannotti F (2012) Injecting discrimination and privacy awareness into pattern discovery. In: 2012 IEEE 12th International Conference on Data Mining Workshops. IEEE Computer Society, pp 360–369
27. 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. IEEE Computer Society, pp 352–359
28. Hajian S, Domingo-Ferrer J, Farràs O (2014) Generalization-based privacy preservation and discrimination prevention in data publishing and mining. Data Min Knowl Discov 28(5–6):1158–1188
29. Hay M, Rastogi V, Miklau G, Suciu D (2010) Boosting the accuracy of differentially private histograms through consistency. Proc VLDB 3(1):1021–1032
30. Hundepool A, Domingo-Ferrer J, Franconi L, Giessing S, Schulte-Nordholt E, Spicer K, de Wolf P-P (2012) Statistical disclosure control. Wiley, New York
31. Kamiran F, Calders T (2011) Data preprocessing techniques for classification without discrimination. Knowl Inf Syst 33(1):1–33
32. Kamiran F, Calders T, Pechenizkiy M (2010) Discrimination aware decision tree learning. In: Proceedings of IEEE International Conference on Data Mining, pp 869–874
33. Kamiran F, Karim A, Zhang X (2010) Decision theory for discrimination-aware classification. In: ICDM IEEE, pp 924–929
34. Kamiran F, Zliobaite I, Calders T (2013) Quantifying explainable discrimination and removing illegal discrimination in automated decision making. Knowl Inf Syst 35(3):613–644
35. Kamishima T, Akaho S, Asoh H, Sakuma J (2012) Fairness-aware classifier with prejudice remover regularizer. In: ECML/PKDD. Lecture Notes in Computer Science vol 7524. Springer, Berlin pp 35–50
36. Kantarcioglu M, Jin J, Clifton C (2004) When do data mining results violate privacy? In: KDD. ACM Press, New York, pp 599–604
37. Lee J, Clifton C (2012) Differential identifiability. In: KDD 2012. ACM Press, New York, pp 1041–1049
38. Li N, Qardaji WH, Su D, Cao J (2012) PrivBasis: frequent itemset mining with differential privacy. Proc VLDB 5(11):1340–1351
39. Li N, Li T, Venkatasubramanian S (2007) t-Closeness: privacy beyond k-anonymity and l-diversity. In: IEEE 23rd International Conference on Data Engineering (ICDE) pp 106–115
40. Li W, Han J, Pei J (2001) CMAR: accurate and efficient classification based on multiple class-association rules. In: Proceedings of the 2001 IEEE International Conference on Data Mining (ICDM), pp 369–376
41. Loung BL, Ruggieri S, Turini F (2011) k-NN as an implementation of situation testing for discrimination discovery and prevention. In: ACM international conference on knowledge discovery and data mining (KDD 2011). ACM Press, New York, pp 502–510
42. 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
43. McSherry F, Talwar K (2007) Mechanism design via differential privacy. In: Proceedings of the 48th IEEE Symposium on Foundations of Computer Science (FOCS), pp 94–103
44. Pasquier N, Bastide Y, Taouil R, Lakhal L (1999) Discovering frequent closed itemsets for association rules. In: Proceedings of the 7th International Conference on Database Theory
45. Pedreschi D, Ruggieri S, Turini F (2008) Discrimination-aware data mining. In: Proceedings of the 14th ACM International Conference on Knowledge Discovery and Data Mining (KDD). ACM Press, New York, pp 560–568
46. Pedreschi D, Ruggieri S, Turini F (2009) Measuring discrimination in socially-sensitive decision records. In: Proceedings of the SIAM International Conference on Data Mining (SDM). SIAM, pp 581–592
47. Pedreschi D, Ruggieri S, Turini F (2009) Integrating induction and deduction for finding evidence of discrimination. In: 12th ACM International Conference on Artificial Intelligence and Law (ICAIL). ACM Press, New York, pp 157–166
48. 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, volume 3 of studies in applied philosophy. Epistemology and Rational Ethics. Springer, Berlin, p 4357
49. Ruggieri S, Pedreschi D, Turini F (2010) Data mining for discrimination discovery. ACM Trans Knowl Discov Data (TKDD) 4(2), Article 9
50. Samarati P (2001) Protecting respondents’ identities in microdata release. IEEE Trans Knowl Data Eng 13(6):1010–1027
51. Soria-Comas J, Domingo-Ferrer J (2012) Sensitivity-independent differential privacy via prior knowledge refinement. Int J Uncertain Fuzziness Knowl Based Syst 20(6):855–876
52. Sweeney L (2002) k-Anonymity: a model for protecting privacy. Int J Uncertain Fuzziness Knowl Based Syst 10(5):557–570
53. United States Congress, US Equal Pay Act (1963) http://archive.eeoc.gov/epa/anniversary/epa-40.html
54. Zemel RS, Wu Y, Swersky K, Pitassi T, Dwork C (2013) Learning fair representations. ICML 3:325–333
55. Zeng C, Naughton JF, Cai J-Y (2012) On differentially private frequent itemset mining. PVLDB 6(1):25–36
56. Zliobaite I, Kamiran F, Calders T (2011) Handling conditional discrimination. In: Proceedings of 13th IEEE International Conference on Data Mining (ICDM) pp 992–1001