1. Secure Incremental Maintenance of Distributed Association Rules

2. Agenda Introduction Secure Technologies Problem Definition Our algorithm Experiments Conclusions

3. Introduction Association Rules – A means to identify patterns and trends Secure Distributed Association Rules – Privacy is concerned – Restricted usage of some information Maintenance of environment – Association rules with more sites – Use past results to reduce workload

4. Secure Data Mining Approach 1: Data Obfuscation – Association rules from modified data – Simple algorithms but may get false rules Approach 2: Secure Protocols – Complex communication – Difficult and costly algorithms but get accurate rules – Balance between cost and privacy

5. Secure Technologies Secure Sum – There are n sites – Each site holds a private number – Compute the sum of a group of sites Secure Union – There are n sites – Each site holds a private set of items – Compute the union of sets

6. Secure Sum Example Site 1 Site 2Site 3 10 811 17 – 28 mod 40 = 29 38 9 17 Upper Bound: 40 R = 28 38 + 11 mod 40 = 9

7. Secure Technologies Secure Comparison – Two sites – A site holds a number a, another holds a number b – Check if a >= b without letting anyone knows the value of a and b

8. Problem Definition There are n old sites – Knows the association rules in these sites There are r new sites – Requires update of association rules in new environment Maintain the privacy as well

9. Privacy? What to protect? Different requirements in different situation Basic requirements – Protect individual transaction – Protect individual site information Local large itemsets, counts for itemsets Secure Multi-party computation – The process does not reveal any other useful information except the information that can be derived from own input and the final result

10. Algorithms Secure Incremental Maintenance of Distributed Association Rules (SIMDAR) – Mining association rules with basic privacy level More Secure Incremental Maintenance of Distributed Association Rules (MSIMDAR) – Mining association rules under the definition of Secure Multiparty computation

11. SIMDAR: What we know? (Assumption) Original Large Itemset L k is available Total count for each old large Itemset is known All sites follow a semi-honest model – They follow the rules, but may try to guess other ’ s information based on the received data (intermediate messages) No collusion among any sites – Sites do not exchange intermediate information

12. Algorithm - SIMDAR To find the large itemsets – Generate the candidate sets – Count on the candidates – Summing counts – Check for large itemset Check if an association rule holds – Easy with counts available

13. Generate the candidates C1 = I For Ck, – Each new site generates its own candidate set with own (k-1)th locally large and globally large itemsets Secure Union to find the candidate sets from the new sites Union with L k

14. Summing on candidates Partition into 2 groups – P k : in L k – Q k : not in L k For P k, we got the original count, just add up the count in new sites using secure sum (no scan on old sites)

15. Summing Count for Q k First summed up in new sites, we get a count If the itemset is large in new sites, send to old sites for scan Otherwise, prune away

16. Information Protected by SIMDAR Individual transaction – We never access to individual transaction of others Large Itemset of specific site – They are input to Secure Union Count of each Itemset on each site – They are input to Secure Sum

17. MSIMDAR: for Higher privacy level Final result: global association rules Input: Site database Other information should be protected Cannot reveal large itemsets? – Costly checking – We treat the large itemsets as part of the result

18. MSIMDAR Target: Global large itemsets and association rules Useful information revealed by SIMDAR – Total Counts of itemsets – Original results of large itemset to new sites – New Candidates at new sites to old sites Add fake itemset to hide the actual supported itemsets

19. MSIMDAR Hiding the total count of an itemset – Do we really need to find out the total count? Protect the large itemsets of the original results – Use a more complex protocol

20. MSIMDAR – Adding Total excess count: – X.excess = X.count – s% |DB| Instead of summing X.count i, we sum the excess count X.excess i – Even revealed, we cannot know the count and database size Checking for large itemsets after Secure Sum – Sa (the first site) holds random key Rx – Sb (the last site) holds (X.count – s% |DB| + Rx) – Secure Comparison between Sa and Sb

21. Storage We can reuse it in future and we need it in the future – Checking for association rules requires counting information – Prepare for next update

22. Storage Commonly used method – Each site holds their own information Count for each itemset Database size – need to calculate the total count each time

23. Storage We first sum the total database size |DB| using Secure Sum – S u (first site) holds the key of secure sum R t – S v (last site) get the sum |DB| + R t For each itemset X, we store also – The protecting key R x – The protected excess count X.excess + R x

24. Reusing the count Checking association rules – A.count – c% B.count > 0 Can be derived by six stored numbers – N1 + (-1)N2 + (-c%)N3 + (c%)N4 + (c%-1)s%N5 + (1-c%)N6 N1 = A.excess + R a N2 = R a N3 = B.excess + R b N4 = R b N5 = |DB| + R t N6 = R t Secure sum and secure comparison

25. Avoiding new sites knowing past results Generating the candidates is similar except an old site will join to the Secure Union process For counting, two old sites will join Define: – P k = L k intersect C k – Q k = C k – P k – Note that the new sites should not be able to distinguish P k and Q k

26. Adding counts in new site

27. Adding for P k Old sites A B New Sites Random Key Sum Protected excess Secure Compare A

28. Adding for Q k Old sites A B New Sites 0 Sum 0 Secure Compare A

29. New site pruning New sites sends the count to an old site to continue We got final excess count for P k – Comparison means if the itemset is large in all sites We got excess count in new sites for Q k – Comparison means if the itemset is large in new sites

30. Experiments 3 programs – With privacy but no maintenance (SEC) – No Privacy but maintenance (MAN) – With privacy and maintenance (MSIDMAR) Environment – P4 1.7GHz under Linux – Each site is simulated by an individual computer Measure – CPU time

31. DB size

32. Support

33. Ratio Total 12:39:66:93:12

34. Ratio

35. Analysis Process time at new sites takes much longer – About 3 time to 5 times of that of old sites Cost overhead due to secure algorithm – At old sites, average 10% of total cost – At new sites, average 6% of total cost – Both decrease in proportion with increase in db size

36. Conclusion We have proposed algorithms to solve the maintenance problem at different privacy level – All can give a more efficient solution than simply ignoring the past results As the number of sites are most likely to increase – The load on old sites will be low relatively to new sites – High entrance cost but low maintenance cost

37. End