Privacy Preservation Issues in Association Rule Mining in Horizontally Partitioned Databases
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This document discusses two algorithms for mining association rules from horizontally partitioned databases while preserving privacy. The first algorithm is a two-phased approach that uses encryption for mining large itemsets in the first phase and then introduces random numbers to preserve privacy in the second phase. The second algorithm uses a technique called CK Secure Sum that breaks each site's data into segments and changes neighbors between sites in each round to limit information leakage. Both algorithms aim to allow association rule mining across distributed datasets without revealing private information from individual sites.
Privacy Preservation Issues in Association Rule Mining in Horizontally Partitioned Databases
- 1. Association Rule Mining with Privacy Preservation In Horizontally Distributed Databases Group 1 – Abhra Basak, Apoorva Kumar, Sachin K. Saini, Shiv Sankar, Suraj B. Malode
- 2. Introduction Look before you leap
- 4. Before we start mining! trends or patterns in large datasets extracting useful information useful and unexpected insights analyze and predicting system behavior Data Mining Scalability ? Artificial Engineering Machine Learning Statistics Database Systems
- 5. Association Rule Learning By Rakesh Agarwal, IBM Almaden Research Center
- 6. • 80% of people who buy bread + butter, buy milk • {Bread, Butter} → {Milk} What is an Association Rule? Antecedent Consequent Antecedent Consequent
- 7. Definitions • 80% of people who buy bread + butter, buy milk • {Bread, Butter} → {Milk} Antecedent • Prerequisites for the rule to be applied Consequent • The outcome Support • Percentage of transaction containing the itemset Confidence • Faction of transaction satisfying the rule
- 8. • Two different forms of constraints are used to generate the required association rules • Syntactic Constraints: Restricts the attributes that may be present in a rule. • Support Constraints: No of transactions that support a rule from the set of transactions. Constraints
- 9. Association Rule Learning in Large Datasets large datasets • To find association rules Generating Large Itemset • combinations of itemsets which are above a minimum support threshold Generating Association Rules •Mining all rules which are satisfied in that itemset
- 10. Association Rule Learning in Distributed Datasets And Privacy Preservation
- 11. • Most tools used for mining association rules assume that data to be analyzed can be collected at one central site. • But issues like Privacy Preservation restrict the collection of data. • Alternative methods for mining have to be devised for distributed datasets to the mining process feasible while ensuring privacy. Preview
- 12. • Dataset • Combined data of Twitter and Facebook • Rule • How many percentage of people login into a social networking site and post within the next 2 minutes? Privacy Preservation
- 13. • Horizontally Partitioned (Example: Insurance Companies) • Rule Being Mined: Does a procedure have an unusual rate of complication? • Implications: • A company may have high cases of the procedure failing and they may change policies to help. • At the same time if this rule is exposed it may be a huge problem for the company. • The risks outweigh the gains. Privacy Preservation Patient ID Disease Prescription Effect Patient ID Disease Prescription Effect Patient ID Disease Prescription Effect Company A Company C Company B
- 14. • Vertically Partitioned Privacy Preservation Credit Card No. Bought tablet 2365987545623526 1 3639871526589414 1 4365845698742563 1 5962845632561200 1 6621563289657412 1 Credit Card No. Bought TCover 2365987545623526 0 7639871526589414 1 4365845698742563 1 9962845632561200 0 6621563289657412 1 Common Property Not One We can exploit.
- 15. Mining of Association Rules In Horizontally Partitioned Databases
- 16. What we want • Computing Association Rules without revealing private information and getting • The global support • The global confidence What we have • Only the following information is available • Local Support • Local Confidence • Size of the DB Fundamental Steps Even this information may not be shared freely between sites. But we’ll get to that.
- 18. • It protects individual privacy but each site has to disclose information. • It reveals the local support and confidence in a rule at each site. • This information if revealed can be harmful to an organization. Problems with the approach
- 19. • We will be exploring two algorithms that have been used. • One algorithm that has been used incorporates encryption with data distortion while data sharing between sites. • The second algorithm uses a particular Check Sum as the method of encryption. Introducing the two Algorithms
- 20. Algorithm Uno Some people are honest
- 21. • Phase 1: Uses encryption for mining of the large itemsets • Phase 2: Uses a random number to preserve the privacy of each site (assuming a 3 or more party system) Two phased algorithm
- 22. Phase 1: Commutative Encryption
- 23. Phase 2: Data Distortion Site A ABC:5 Size=100 Site B ABC:6 Size=200 Site C ABC:20 Size=300 R+count-5%*Size =17+5-5%*100 13+20-5%*300 17+6-5%*200 13 17 18 >= R R=17
- 24. • Doesn’t work for a 2 party system • Assumes honest parties • Assumes Boolean responses to variable for support of rules rather than a subjective or weighted approach. • As the no of candidate itemsets increases the encryption overhead increases. • The encryption overhead also varies directly proportional to the no of sites or partitions. Problems with the Algorithm I got ……
- 25. Algorithm Dua Don’t trust anyone
- 26. • Primarily used for to tackle semi honest sites. • Data of each site is broken down into segments. • Two interleaved nodes have a probability of hacking the one in between them. • The neighbors are changed for each round. Hence, they can only obtain one such segment. CK Secure Sum
- 28. Conclusion The moral of the story...
- 29. Before you leave • It is interesting that association rules play a vital role in data mining. • Through this, what appears to be unrelated can have a logical explanation through careful analysis. • This aspect of data mining can be very useful in predicting patterns and foreseeing trends in consumer behavior, choices and preferences. • Association rules are indeed one of the best ways to succeed in business and enjoy the harvest from data mining.
- 30. There are no dumb questions (No questions please shhhh…)