Illustration: 3-Party Secure Sum Compare, match, and analyze data from different organizations...
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Transcript of Illustration: 3-Party Secure Sum Compare, match, and analyze data from different organizations...
Illustration: 3-Party Secure Sum
Compare, match, and analyze data from different organizationswithout disclosing the private data to any other party
Experimental Results
Privacy Preserving Distributed Data Mining: A Game Theoretic ApproachKamalika Das*, Hillol Kargupta**
*University of Maryland, Baltimore County**University of Maryland, Baltimore County & Agnik, LLC
Organization A Data
DataData
Organization COrganization B
Multi-Party PPDM as Games
Computation Strategies: Perform or not perform local computation
Communication Strategies: Send/Receive messages to other nodes in the network or not
Privacy Compromise due to Collusion: Whether or not to be part of a colluding group to reveal others’ private data
Personalized Privacy in Distributed Environment
Privacy: a social concept
Amount of resources vary across users
Distributed multi-objective optimization gives parameter values for privacy model
Mechanism design to incorporate penalty in protocol
Penalty for Desired Equilibrium
Centralized Control Global Synchronization Trusted Third Party Auditing Device
Distributed Control Distributed Decision Keep nodes in the system
References:
[1] H. Kargupta, K. Das, and K. Liu. Multi-party, Privacy-Preserving Distributed Data Mining Using a Game Theoretic Framework. Proceedings of the 11th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD'07), Warsaw, Poland, Lecture Notes in Artificial Intelligence 4702, Springer 2007, pp 523-531.
[2] K. Das, K. Bhaduri and H. Kargupta. A Distributed Asynchronous Local Algorithm Using Multi-Party Optimization based Privacy Preservation (in communication)[3] K. Das, K. Bhaduri and H. Kargupta. Address-Free Communication and Scalable Privacy Preserving Data Mining Algorithms for Peer-to-Peer Networks (in communication)[4] K. Das and H. Kargupta. A Game-Theoretic Framework for Distributed Privacy Preserving Secure Sum Computation (in communication)
Wecanarrangethesitesin thefollowingorder:
v1v2:::vs¡ k¡ 1| {z }honest sites
vi vi+1:::vi+k| {z }colludingsites
Wehaves¡ k¡ 1X
j =1
vj| {z }
denotedbyX
+vi = v¡i+kX
j =i+1
vj| {z }denotedbyC
;
wherev isthetotal sumof thes values.
Secure Sum with Penalty Algorithm
1.Network has n nodes: nodes are good (n-k) or bad (k). Bad nodes form one colluding group
2.Good nodes solve local objective function based on estimated threat, desired privacy and cost constraints to decide on amount of penalty (k’).
3.To penalize bad nodes, good nodes split their data into k’ parts.
4.Bad nodes turn good at end of sum computation if cost is too high.
Site worried about privacy
ui (f ¾i ; ¾¡ ig) = wi;mci;m(M i ) + wi;rci;r (R i ) + wi;sci;s(S i ) + wi;gci;g(G i );
~ui(f ¾i;¾¡ ig) = ui(f ¾i;¾¡ ig) ¡ ¤®k0, where ®> 0
WORKS FOR REPEATED GAMES
k is the number of colluders Each party has an array of n
numbers Compute n sums without divulging
individual numbers Scenario: Sequence of secure sum
computations
v1
v2z2=(z1+v2) mod N v3
z1=(R+v1) mod N z3=(z2+v3) mod N
Overall utility for secure sum computation with punishment strategy. The optimal strategy takes a value of k=1.
Overall utility for classical secure sum computation. The optimal strategy takes a value of k>1.
Rate of decrease of bad nodes
Collusion Utility vs. Total Cost
Applications
Distributed privacy preserving ranking: Application in P2P web advertising
Distributed privacy preserving feature selection: Application in P2P decision tree induction
DIADIC