Privacypreserving Data Mining: current research and trends


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1 Privacypreserving Data Mining: current research and trends Stan Matwin School of Information Technology and Engineering University of Ottawa, Canada
2 Few words about our research Universit[é y] [d of] Ottawa is the largest bilingual university in Canada Applied research and tech transfer Profiles of digital game players/ active learning, cotraining, instance selection Classification of medical abstracts Privacyaware Data Mining Text Analysis and Machine Learning group Learning with previous knowledge 2
3 Plan What is [data] privacy? Privacy and Data Mining Privacypreserving Data mining: main approaches Anonymization Obfuscation Cryptographic hiding Challenges Definition of privacy Mining mobile data Mining medical data 3
4 Privacy Freedom to be left alone ability to control who knows what about us [Westin; Moor] [=database views] Jeopardized with the Internet greased data Moral obligation for the community to find solutions 4
5 Privacy and data mining Individual Privacy Nobody should know more about any entity after the data mining than they did before Approaches: Data Obfuscation, Value swapping Organization Privacy Protect knowledge about a collection of entities Individual entity values are not disclosed to all parties The results alone need not violate privacy 5
6 Basic ideas Camouflage Hiding obfuscation kanonymity 6
7 Why naïve anonymization does not work The [Sweeney 0] experiment purchased the voter registration list for Cambridge, MA 54,805 people 69% unique on postal code and birth date; 87% USwide with all three Also, we do not know with what other data sources we may do joins in the future Solution: kanonymization 7
8 Randomization Approach Overview Alice s age 30 70K K Randomizer Randomizer Age 30 becomes 65 (3035) Salary 70K becomes 20K 65 20K K Reconstruct Distribution of Age Classification Algorithm Reconstruct Distribution of Salary... Model 8
9 Reconstruction Problem Original values x, x 2,..., x n from probability distribution (unknown) To hide these values, we use y, y 2,..., y n from known distribution Y Given x y, x 2 y 2,..., x n y n the probability distribution of Y Estimate the probability distribution of. 9
10 Intuition (Reconstruct single point) Use Bayes' rule for density functions 0 V 90 Age Original distribution for Age Probabilistic estimate of original value of V 0
11 Works well 200 Number of People Original Randomized Reconstructed Age
12 Recap: Why is privacy preserved? Cannot reconstruct individual values accurately. Can only reconstruct distributions. 2
13 Classification Naïve Bayes Assumes independence between attributes. Decision Tree Correlations are weakened by randomization, not destroyed. 3
14 Decision Tree Example Age Salary Repeat Visitor? 23 50K Repeat 7 30K Repeat 43 40K Repeat 68 50K Single 32 70K Single 20 20K Repeat Yes Repeat Age < 25 Repeat Yes No Salary < 50K No Single 4
15 Decision Tree Experiments 00% Randomization Level 00 Accuracy Original Randomized Reconstructed Fn Fn 2 Fn 3 Fn 4 Fn 5 00% privacy: attribute cannot be estimated (with 95% confidence) any closer than the entire range for the attribute 5
16 Issues For very high privacy, discretization will lead to a poor model Gaussian provides more privacy at higher confidence levels In fact, it can be derandomized using advanced control theory approach [Kargupta 2003] 6
17 Association Rule Mining Algorithm [Agrawal et al. 993] L. = large itemsets 2. for ( k = 2; Lk φ; k ) do begin 3. C ( ) k = apriori gen Lk 4. for all candidates c C k do begin 5. compute c.count 6. end 7. Lk = { c Ck c. count min sup} 8. end 9. Return L = U L k k c.count is the frequency count for a given itemset. Key issue: to compute the frequency count, we needs to access attributes that belong to different parties. 7
18 An Example c.count is the vector product. Let s use A to denote Alice s attribute vector and B to denote Bob s attribute vector. AB is a candidate frequent itemset, then c.count = A B = 3. How to conduct this computation across parties without compromising each party s data privacy? Alice Bob 0 A 0 B 8
19 Homomorphic Encryption [Paillier 999] Privacypreserving protocols are based on Homomorphic Encryption. Specifically, we use the following additive homomorphism property: e ( m ) e( m2 ) L e( mn ) = e( m m2 L m n ) Where e is an encryption function and m i is the data to be encrypted and e( ) 0. m i 9
20 Digital Envelope [Chaum85] A digital envelope is a random number (a set of random numbers) only known by the owner of private data. V V R VV 20
21 The Objective Privacy Correctness Efficiency Solution Homomorphic Encryption Digital Envelope 2
22 Frequency Count Protocol Assume Alice s attribute vector is A and Bob s attribute vector is B. Each vector contains N elements. A i : the ith element of A. B i : the ith element of B. One of parties is randomly chosen as a key generator, e.g, Alice, who generates (e, d) and an integer > N. e and will be shared with Bob. Let s use e(.) to denote encryption and d(.) to denote decryption. 22
23 Alice A R A R 2 2 AN R N Digital envelopes R R,, 2 R N L A set of random integers generated by Alice 23
24 Alice A R A 2 R 2 AN RN e A R ) e A R ) e( AN RN ) ( (
25 Alice e ( A R ) e A R ) e( A R ) ( 2 2 N N Bob 25
26 26 ) ( B R A e W = ) ( B R A e W = N N N N B R A e W = ) ( Bob ) ( ) ( 0 0 R A e B R A e W B W B i i i i i i i i i = = = = =
27 Bob multiplies all the W i s for those B i s that are not equal to 0. In other words, Bob computes the multiplication of all nonzero W i s, e.g., W where. W i 0 = Wi W = W W L 2 W j 27
28 28 W j W W W = L 2 ] ) ( [ ] ) ( [ ] ) ( [ j j j B R A e B R A e B R A e = L
29 29 W j W W W = L 2 ] ) ( [ ] ) ( [ ] ) ( [ 2 2 = R A e R A e R A e j j L
30 30 W j W W W = L 2 ) ( ) ( ) ( 2 2 R A e R A e R A e j j = L ) ) ( ( 2 2 R R R A A A e j j = L L According to the property of homomorphic encryption
31 Bob generates an integer R'. Bob then computes W ' = W e( R' ) According to the property of homomorphic encryption = e( A A2 L Aj ( R R2 L R j R') ) Alice 3
32 The Final Step W ' Alice decrypts and computes modulo. c. count = d( e( A A2 L Aj ( R R2 L R j R') )) mod ( A (( R A 2 R L 2 L R A ) N < j j R') )mod = 0 She then obtains A for those A j for which A2 L Aj corresponding B j are 0, which is = c.count 32
33 Privacy Analysis Goal: Bob never sees Alice s data values. All the information that Bob obtains from Alice is e( A R ), e( A2 R2 ), L, e( AN RN ). Since Bob doesn t know the decryption key d, he cannot get Alice s original data values. 33
34 Privacy Analysis Goal: Alice never sees Bob s data values. The information that Alice obtains from Bob is W = e( A A L A ( R R L R R') ) for those =. ' 2 j 2 j Alice computes d( W ') mod. She only obtains the frequency count and cannot know Bob s original data values. B i 34
35 Complexity Analysis Linear in the number of transactions The total number elements in each attribute vector where N is the total number transactions and α is the number of bits for each encrypted element. α( N ) 35
36 Complexity Analysis Linear in the number of transactions The computational cost is (0N 20 g) where N is the total number transactions and g is the computational cost for generating a key pair. 36
37 Other PrivacyOriented Protocols MultiParty Frequency Count Protocol [Zhan et al (a)] MultiParty Summation Protocol [Zhan et al (f)] MultiParty Comparison Protocol [Zhan et al (a)] MultiParty Sorting Protocol [Zhan et al (a)] 37
38 What about the results of DM? Can DM results reveal personal information? In some cases, yes [Atzori et al. 05]: Suppose an association rule is found: a a a a [sup = 80, conf = 98.7%] This means then sup({ a, a, a, a }) = sup({ a, a, a, a }) sup({ a, a2, a 3}) = = = 8.05 therefore a a2 a3 a4 has support=, and identifies one person!! 38
39 They propose an approach called kanonymous patterns and an algorithm (inference channels) which detects violations of kanonymity The algorithm is expensive computationally We have a new approach which embeds k anonimity into the concept lattice association rule algorithm [Zaki, Ogihara 98] 39
40 Conclusion Important problem, challenge for the field Lots of creative work, but lack of systematic approach Medical data particularly sensitive, but also makes deidentification easier: genotypephenotype inferences, locationvisit patterns, family structures, etc. [Malin 2005] Lack of an operational, agreed upon definition of privacy: inspiration in economics? 40
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