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

3. The Flow
Association
Rule Mining
Privacy
Preservation
Horizontally
Distributed
Datasets

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.

17. Calculating Required Values

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

27. P1
P2
P3
P4
Changing Neighbors
P1
P2
P4
P3
P1
P4
P2
P3
Round 1
Round 2
Round 3

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…)