A discussion on the research paper 'An Efficient Approximate Protocol for Privacy-Preserving Association Rule Mining' by 'Murat Kantarcioglu, Robert Nix , and Jaideep Vaidya'

1. AN EFFICIENT APPROXIMATE PROTOCOL
FOR
PRIVACY PRESERVING ASSOCIATION
RULE MINING
MURAT KANTARCIOGLU, ROBERT NIX AND JAIDEEP VAIDYA (2009)
- BY
PUSHPALANKA JAYAWARDHANA
158217G

2. CONTENTS
• Introduction
• Two Typesof Techniques
• Problem
• Proposing Solution
• Related Work
• Bloom Filters
• Redefinethe Problem
• Approximate Threshold Dot ProductAlgorithm
• Security
• Computational and Communicational Cost
• Experimental Results
• Accuracy
• Efficiency
• Conclusion

3. INTRODUCTION
• Association Rule Mining
• An important data mining model studied extensively by the database and
data mining community
• A method for discovering interesting relations between variables in large
databases
• "Beer and diaper" story
• Buying Diapers==> Buying Beer
• Privacy Preserving in Association Rule Mining
• More parties are interested in learning the global association rules
• None is willing to reveal the data at individual sites

4. TWO TYPES OF TECHNIQUES
• Perturbation Based methods
• Locally perturb data before delivering to the data miner
• Special techniques are used to reconstructthe original distribution
• Mining algorithm needs to be modified to consider that data is perturbed
• Have security concerns
• Secure Multiparty Computation Techniques
• Each party builds decision tree
• Only the final decision tree is shared, not any other data
• Use cryptographic techniques for security
• Computationally intensive

5. PROBLEM?
How can we mine data in an
efficient and provably secure
way?

6. PROPOSING SOLUTION
An approximate protocol for
computing the dot product of two
vectors owned by two different
parties

7. RELATED WORK
• A similar approximation protocol is already proposed with
sampling techniques
• A solution is present with bloom filters
• Rule mining is done centrally
• Goethals' s encryption mechanism
• simple and secured
• Calculate exact dot product
• Run time O(n)

8. BLOOM FILTERS
• A probabilistic data structure
• Used to test on membership of
a set
• False positives are possible
• No false negatives
• Can be used to approximate
the intersection size between
two sets

9. REDEFINE PROBLEM
• Compute the scalar product
• Checks if the scalar product of two distributed vectors is greater than some threshold
X1 . X2 = |S1 ∩ S2| ≥ t

10. APPROXIMATE THRESHOLD DOT PRODUCT ALGORITHM
• Each party creates own bloom filter, using common parameters.
• size of the bloom filter - m
• hash functions - h1, h2, ..................., hk
• Participate in the secure dot product algorithm using private bloom
filters and get the random shares of the dot product result
• each party participates in secure multiplication protocol using private
dot product results to get the random share of the multiplication result
• Finally, each party participate in a secure comparison protocol to
approximate the final result.

11. SECURITY
• Preserved under following assumptions
• Parties are semi-honest
• Dot product, multiplication and comparison protocols are
secure

12. COMPUTATIONAL AND COMMUNICATIONAL COST
• O(nk) for hashing for bloom filters, rest is O(1)
• Hashing cost is negligible compared to public key operations
• m<<n --> faster
• Flexible to use if a better secure dot product computing protocol if found in
the future
• communication cost propotional to m --> low cost

13. EXPERIMENTAL RESULTS
• Consider effect of,
• vector length (l),
• vector density (d)
• the actual intersection of the two vectors (i)
• the bloom filter parameters
• m (length of filter)
• k(number of hash functions)
on the performance of the algorithm.

14. ACCURACY -1
Increase k --> increase distortion --> less
accuracy
(when filter length is small )

15. ACCURACY -2
Increase filter length --> high
accuracy
(Less distortion and collision )

16. ACCURACY -3
Even for a large vector, same
accuracy can be achieved with sub-
linear increase in filter length

17. ACCURACY -4
• At 0 density no error
• Drastically increase error at high
densities
• Good for sparse vectors

18. ACCURACY -5
< 1 % error all the time

19. EFFICIENCY
Compared to exact
version,
27m : 57s Vs 4m : 04s
at run time

20. CONCLUSIONS
• Propose an efficient and secure protocol to approximately compute scalar
product in a privacy preserving manner.
• Efficiency is gained by allowing an approximation than an exact answer
• Extending to work with more than 2 parties is a future work

21. Q & A