1. UT DALLAS Erik Jonsson School of Engineering & Computer Science FEARLESS engineering Incentive compatible Assured Data Sharing & Mining Murat Kantarcioglu

2. FEARLESS engineering Incentives and Trust in Assured Information Sharing Combining intelligence through a loose alliance Bridges gaps due to sovereign boundaries Maximizes yield of resources Discovery of new information through correlation, analysis of the ‘big picture’ Information exchanged privately between two participants Drawbacks to sharing Misinformation Freeloading Goal: Create means of encouraging desirable behavior within an environment which lacks or cannot support a central governing agent

3. FEARLESS engineering Possible Scenarios You may verify the shared data, and issue fines if the data is wrong – This is easy You may verify the share data but cannot issue fines – Little bit harder You may only verify some aggregate result – Hardest

4. FEARLESS engineering Game Matrix Play (agent j)Do Not Play TruthLie Play (Agent i) Truth 0000 Lie 0000 Do Not Play 0000 0000 0000 Value of information Minimal verification probability Cost of Verification Trust value Agent type

5. FEARLESS engineering Behaviors Analyzed in Data Sharing Simulations NameStrategyVerification?Punishment?Comments HonestTruthNo Optimistic, maximizes returns DishonestLieNo Takes advantage of other players, trumps Honest in 1 on 1 RandomTruth, LieNo Chaotic, chooses either with equal probability Tit-for-TatTruth, LieAlwaysSpecial Mirrors other players’ actions, starts by selecting Truth LivingAgentTruthTrust-basedNo trading Verifies activity according to trust ratings, will cease activity for number of rounds with player who is caught lying LiarTruth, LieTrust-basedNo trading Identical to LivingAgent but lies with small probability SubtleLieTruth, LieTrust-basedNo trading Identical to Liar, except lies whenever information value reaches certain threshold

6. FEARLESS engineering Simulation Results We set δ min = 3, δ max = 7, C V = 2 Lie threshold is set 6.9 Honest behavior wins %97 percent of the time if all behaviors exist. Experiments show without LivingAgent behavior, Honest behavior cannot flourish. Please see the following paper for mode details: “Incentive and Trust Issues in Assured Information Sharing” Ryan Layfield, Murat Kantarcioglu, and Bhavani Thuraisingham International Conference on Collaborative Computing 2008

7. FEARLESS engineering Verifying Final Result: Our Model Players P 1....P n : Each has some data (x 1...x n ), and Goal: compute a data mining function, D(x 1,...,x n ) that maximizes the sum of the participants valuation function. Player P t : Mediator between parties, computes the function securely, and has test data x t Players value privacy, correctness, exclusivity Problem: How do we ensure that players share data truthfully?

8. FEARLESS engineering Assumption The best model that maximizes sum of the valuation function is the model built by using the submitted input data. Formally: Given submitted valuation functions and submitted data –D(x) = argmax m  M (  {k} v k (m) ) for any set of players

9. FEARLESS engineering Mechanism Reservation utility normalized to 0 u i (m) = v i (m) – p i (v i,v -i ) [u = utility] [v = valuation] [p = payment] p i (v i,v -i ) = argmax m’  M (  {k!=i} (v k (m’)) –  {k!=i} (v k (m)) v i (m) = max{0,acc(m)-acc(D(x i )} – c(D) –c is the cost of computation, acc is accuracy

10. FEARLESS engineering Mechanism We compute p i using the independent test set held by P t Intuitively, mechanism rewards players based on their contribution to the overall model This is a VCG mechanism, proved incentive compatible, under our assumption

11. FEARLESS engineering Experiments Does this assumption hold for normal data? Methodology 4 data sets from UCI Repository 3-party vertical partitioning, naïve-Bayes classifiers Determine accuracy and payouts Payouts estimated by acc(classifier) – acc(classifier without player i’s data) – constant cost Once with all players truthful Once for each player and for each amount of perturbation (1%, 2%, 4%, 8%, 16%, 32%, 64%, 100%) 50 runs on each

12. FEARLESS engineering Census-Income (Adult)

13. FEARLESS engineering Census-Income (Adult)

14. FEARLESS engineering Census-Income (Adult)

15. FEARLESS engineering Census-Income (Adult)

16. FEARLESS engineering Census-Income (Adult)

17. FEARLESS engineering Breast-Cancer-Wisconsin

18. FEARLESS engineering Conclusions Does the assumption hold? Not always, but it is very close, and would work as a practical assumption If better model is found through lying, does this hurt or help? Consideration: change the goal; not to prevent lying but to build the most accurate classifier Finding the “right” lie may take too much computation for profitability