Data is shared among different organizations for mutual benefit. Data mining techniques are utilized to discover valuable knowledge for decision-making. However, data mining poses a threat to disclose the sensitive information. Thus, the sensitive knowledge should be concealed before releasing data. The pervious works either address the association rule or utility itemsets hiding problem. This paper focuses on preserving the sensitive utility and frequent itemsets, and a sanitization approach named HUFI is presented. The sensitive itemsets are hidden by reducing their support or utility below the minimum thresholds. For a sensitive itemset, the concept of maximum boundary value is introduced to determine the hidden strategy. Then, a transaction supporting minimal number of non-sensitive itemsets is selected to be sanitized. In such a transaction, a weight is assigned to each item contained in the sensitive itemset, and an item with the highest weight is selected to be modified. We compared HUFI with the state of the art algorithms on various databases. The experiment results show that HUFI outperforms the other algorithms in minimizing the side effects on non-sensitive knowledge and maintaining the database quality after the sanitization process. In addition, the impact of database density on sanitization approaches is observed.
AmiriA., Dare to share: Protecting sensitive knowledge with data sanitization, Decision Support Systems43(1) (2007), 181–191.
2.
Gkoulalas DivanisA.HaritsaJ. and KantarciogluM., Privacy issues in association rule mining, Frequent Pattern Mining, Springer International Publishing, 2014, 369–401.
3.
Gkoulalas DivanisA. and VerykiosV.S., Exact knowledge hiding through database extension, IEEE Transactions on Knowledge and Data Engineering21(5) (2009), 699–713.
4.
CliftonC. and MarksD., Security and privacy implications of data mining, in: Proceedings of the ACM SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery, 1996, pp. 15–19.
5.
O’LearyD.E., Knowledge discovery as a treat to database security, in: Proceedings of the 1st International Conference in Knowledge Discovery and Database, 1991, pp. 107–516.
6.
DasseniE.VerykiosV.S.ElmagarmidA.K. and BertinoE., Hiding association rules by using confidence and support, in: Proceedings of the 4th International Workshop on Information Hiding, 2001, pp. 369–383.
7.
LeeG. and ChenY.C., Protecting sensitive knowledge in association patterns mining, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery2(1) (2012), 60–68.
8.
MoustakidesG.V. and VerykiosV.S., A Max-Min approach for hiding frequent itemsets, in: Proceedings of the 6th International Conference on Data Mining, 2006, pp. 502–506.
9.
MoustakidesG.V. and VerykiosV.S., A MaxMin approach for hiding frequent itemsets, Data and Knowledge Engineering65(1) (2008), 75–89.
10.
MannilaH. and ToivonenH., Levelwise search and borders of theories in knowledge discovery, Data Mining and Knowledge Discovery1(3) (1997), 241–258.
11.
LeH.Q.Arch IntS. and Arch IntN., Association rule hiding based on intersection lattice, Mathematical Problems in Engineering2013 (2013), 1–11.
12.
LeH.Q.Arch IntS.NguyenH.X. and Arch IntN., Association rule hiding in risk management for retail supply chain collaboration, Computers in Industry64(7) (2013), 776–784.
13.
YaoH.HamiltonH.J. and ButzC.J., A foundational approach to mining itemset utilities from databases, in: Proceedings of the 4th SIAM International Conference on Data Mining, 2004, pp. 482–486.
14.
LinJ.C.W.WuT.Y.Fournier VigerP.LinG.ZhanJ. and VoznakM., Fast algorithms for hiding sensitive high-utility itemsets in privacy-preserving utility mining, Engineering Applications of Artificial Intelligence55(C) (2016), 269–284.
15.
YehJ.S. and HsuP.C., HHUIF and MSICF: Novel algorithms for privacy preserving utility mining, Expert Systems with Applications37(7) (2010), 4779–4786.
16.
AtallahM.BertinoE.ElmagarmidA.IbrahimM. and VerykiosV., Disclosure limitation of sensitive rules, in: Proceedings of the 1999 Workshop on Knowledge and Data Engineering Exchange, 1999, pp. 45–52.
17.
LiuM. and QuJ., Mining high utility itemsets without candidate generation, in: Proceedings of the 21st ACM International Conference on Information and Knowledge Management, 2012, pp. 55–64.
18.
ChenM.S.HanJ. and YuP.S., Data mining: an overview from a database perspective, IEEE Transactions on Knowledge and Data Engineering8(6) (1996), 866–883.
19.
ChengP.LinC.W. and PanJ.S., Use HypE to hide association rules by adding items, PLOS One10(6) (2015), e0127834.
20.
ChengP.PanJ.S. and LinC.W., Privacy preserving association rule mining using binary encoded NSGA-II, in: Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2014, pp. 87–99.
21.
ChengP.Chun WeiL.Jeng ShyangP. and IvanL., Manage the tradeoff in data sanitization, IEICE TRANSACTIONS on Information and Systems98(10) (2015), 1856–1860.
22.
ChengP.IvanL.Jeng ShyangP.Chun WeiL. and RoddickJ.F., Hide association rules with fewer side effects, Ieice Transactions on Information and Systems98(10) (2015), 1788–1798.
23.
AgrawalR. and SrikantR., Fast algorithms for mining association rules, in: Proceedings of the 20th International Conference on Very Large Data Bases, 1994, pp. 487–499.
24.
ShahR.A. and AsgharS., Privacy preserving in association rules using a genetic algorithm, Turkish Journal of Electrical Engineering and Computer Sciences22(2) (2014), 434–450.
25.
RajalaxmiR.R. and NatarajanA.M., Effective sanitization approaches to hide sensitive utility and frequent itemsets, Intelligent Data Analysis16(6) (2012), 933–951.
26.
KrishnamoorthyS., Pruning strategies for mining high utility itemsets, Expert Systems with Applications42(5) (2015), 2371–2381.
27.
OliveiraS.R.M. and ZaïaneO.R., Protecting sensitive knowledge by data sanitization, in: Proceedings of the 3th International Conference on Data Mining, 2003, pp. 613–616.
28.
ZidaS.Fournier VigerP.LinC.W.WuC.W. and TsengV.S., EFIM: A highly efficient algorithm for high-utility itemset mining, in: Mexican International Conference on Artifical Intelligence, 2015, pp. 530–546.
29.
HongT.P.LinC.W.YangK.T. and WangS.L., Using TF-IDF to hide sensitive itemsets, Applied Intelligence38(4) (2013), 502–510.
30.
YunU. and KimJ., A fast perturbation algorithm using tree structure for privacy preserving utility mining, Expert Systems with Applications42(3) (2015), 1149–1165.
31.
VerykiosV.S.ElmagarmidA.K.BertinoE. and SayginY., Association rule hiding, IEEE Transactions on Knowledge and Data Engineering16(4) (2004), 434–447.
32.
VerykiosV.S., Association rule hiding methods, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery3(3) (2013), 28–36.
33.
SunX. and YuP.S., A border-based approach for hiding sensitive frequent itemsets, in: Proceedings of the 5th International Conference on Data Mining, 2005, pp. 426–433.
34.
SunX. and YuP.S., Hiding sensitive frequent itemsets by a border-based approach, Journal of Computing Science and Engineering1(1) (2007), 74–94.
35.
WuY.H.ChiangC.M. and ChenA.L., Hiding sensitive association rules with limited side effects, IEEE Transactions on Knowledge and Data Engineering19(1) (2007), 29–42.
36.
SPMF: An open-source data mining library, http://www.philippe-fournier-viger.com/spmf/index.php?link=datasets.php.
