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Privacy-Preserving Data Mining
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  1. Privacy-Preserving Data Mining Rakesh Agrawal Ramakrishnan Srikant IBM Almaden Research Center 650 Harry Road, San Jose, CA 95120 Published in: ACM SIGMOD International Conference on Management of Data, 2000. Slides by: Adam Kaplan (for CS259, Fall’03)

  2. What is Data Mining? • Information Extraction • Performed on databases. • Combines theory from • machine learning • statistical analysis • database technology • Finds patterns/relationships in data • Trains the system to predict future results • Typical applications: • customer profiling • fraud detection • credit risk analysis. Definition borrowed from the Two Crows corporate website: http://www.twocrows.com

  3. CREDIT RISK Age < 25 Salary < 50k High High Low A Simple Example of Data Mining TRAINING DATA Data Mining • Recursively partition training data into decision tree classifier • Non-leaf nodes = split points; test a specific data attribute • Leaf nodes = entirely or “mostly” represent data from the same class • Previous well-known methods to automate classification • [Mehta, Agrawal, Rissanen EDBT’96] – SLIQ paper • [Shafer, Agrawal, Mehta VLDB’96] – SPRINT paper

  4. Where does privacy fit in? • Data mining performed on databases • Many of them contain sensitive/personal data • e.g. Salary, Age, Address, Credit History • Much of data mining concerned with aggregates • Statistical models of many records • May not require precise information from each record • Is it possible to… • Build a decision-tree classifier accurately • without accessing actual fields from user records? • (…thus protecting privacy of the user)

  5. Preserving Data Privacy (1) • Value-Class Membership • Discretization:values for an attribute are discretized into intervals • Intervals need not be of equal width. • Use the interval covering the data in computation, rather than the data itself. • Example: • Perhaps Adam doesn’t want people to know he makes $4000/year. • Maybe he’s more comfortable saying he makes between $0 - $20,000 per year. • The most often used method for hiding individual values.

  6. Preserving Data Privacy (2) • Value Distortion • Instead of using the actual data xi • Use xi + r, where r is a random value from a distribution. • Uniform Distribution • r is uniformly distributed between [-α, +α] • Average r is 0. • Gaussian Distribution • r has a normal distribution • Mean μ(r) is 0. • Standard_deviation(r) is σ

  7. What do we mean by “private?” W = width of intervals in discretization • If we can estimate with c% confidence • The value x lies within the interval [x1, x2] • Privacy = (x2 - x1), the size of the range. • If we want very high privacy • 2α> W • Value distortion methods (Uniform, Gaussian) provide more privacy than discretization at higher confidence levels.

  8. Reconstructing Original Distribution From Distorted Values (1) • Define: • Original data values: x1, x2, …, xn • Random variable distortion: y1, y2, …, yn • Distorted samples: x1+y1, x2+y2, …, xn+yn • FY : The Cumulative Distribution Function (CDF) of random distortion variables yi • FX : The CDF of original data values xi

  9. Reconstructing Original Distribution From Distorted Values (2) • The Reconstruction Problem • Given • FY • distorted samples (x1+y1,…, xn+yn) • Estimate FX

  10. Reconstruction Algorithm (1) • How it works (incremental refinement of FX ) : • The f(x, 0) initialized to uniform distribution • For j=0 until stopping, do • Find f(x, j+1) as a function of f(x, j) and FY • When loop stops, f(x) estimates FX

  11. Reconstruction Algorithm (2) • Stopping Criterion • Compare successive estimates f(x, j). • Stop when difference between successive estimates very small.

  12. Distribution Reconstruction Results (1) Original = original distribution Randomized = effect of randomization on original dist. Reconstructed = reconstructed distribution

  13. Distribution Reconstruction Results (2) Original = original distribution Randomized = effect of randomization on original dist. Reconstructed = reconstructed distribution

  14. Summary of Reconstruction Experiments • Authors are able to reconstruct • Original shape of data • Almost same aggregate distribution • This can be done even when randomized data distribution looks nothing like the original.

  15. CREDIT RISK Age < 25 Salary < 50k High High Low Decision-Tree Classifiers w/ Perturbed Data • When/how to recover original distributions in order to build tree? • Global - for each attribute, reconstruct original distribution before building tree • ByClass – for each attribute, split the training data into classes, and reconstruct distributions separately for each class; then build tree • Local – like ByClass, reconstruct distribution separately for each class, but do this reconstruction while building decision tree

  16. Experimental Results – Classification w/ Perturbed Data • Compare Global, ByClass, Local algorithms against control series: • Original – result of classification of unperturbed training data • Randomized– result of classification on perturbed data with no correction • Run on five classification functions Fn1 through Fn5. (classify data into groups based on attributes)

  17. Results – Classification Accuracy (1)

  18. Results – Classification Accuracy (2)

  19. Experimental Results – Varying Privacy • Using ByClass algorithm on each classification function (except Fn4) • Vary privacy level from 10% - 200% • Show • Original – unperturbed data • ByClass(G) – ByClass with Gaussian perturbation • ByClass(U) – ByClass with Uniform perturbation • Random(G) – uncorrected data with Gaussian perturbation • Random(U) – uncorrected data with Uniform perturbation

  20. Results – Accuracy vs. Privacy (1)

  21. Results – Accuracy vs. Privacy (2) Note: Function 4 skipped because almost same results as Function 5.

  22. Conclusion • Perturb sensitive values in a user record by adding random noise. • Ensures privacy because original values are unknown. • Aggregate functions/classifiers can be accurately performed on perturbed values. • Gaussian distribution of noise provides more privacy than Uniform distribution at higher confidence levels.