1 ACM Student Chapter – Union University Haifei Li, Ph.D. Department of Mathematics and Computer Science Union University Jackson, TN

2 Ties with ACM ACM student member when I was a graduate student at the University of Florida. ACM (regular) member when I am a professor. Faculty advisor for Nyack College student chapter of ACM.

3 Algorithms for Automated Negotiations and Their Applications in Information Privacy Haifei Li and David Ahn Nyack College Patrick C. K. Hung Hong Kong University of Science and Technology

4 Overview Introduction Related Work Algorithm to find Pareto Optimal Solutions Algorithm to Conduct Bilateral Negotiations Credit Card as Private Information Conclusion and Discussion

5 Introduction Negotiation is an active research topic. Hard to automate. Human’s resist to automation effort. Negotiation is often viewed as more of an art, instead of a science. Automated algorithms are needed in order to implement negotiation systems. Negotiation for Information privacy is a new area. Strong assumptions may make the algorithms too idealistic to be useful.

6 Related Work Negotiation Support System  Focus on “support”, not “automate”. Negotiation Agent  No consensus on how agents negotiate, what to be negotiated. Game Theory  rational assumption is too strong Machine Learning  genetic algorithm  bayesian probability Information Privacy  Focus on enforcing companies’ Privacy Policy. Consumer is powerless, at the mercy of Big Brother, Big Buck.

7 utilities for Agent X utilities for Agent Y Points on the Pareto Frontiers are Pareto optimal solutions Pareto Frontier B C DAE Pareto Optimal Solution

8 Negotiation attributes are pre-defined, and they are not dynamically added. The main task of bilateral negotiation is to assign values to negotiation attributes. Utilities (preferences) for some negotiation attribute values of both sides are not monotonically increasing or decreasing. Otherwise, the algorithm is trivial. Delivery schedule as an example:  It is possible that the enterprise’s preference over delivery is not as early as possible (for buyer), or as late as possible (for supplier).  In JIT (Just-In-Time) manufacturing, the raw materials may need to arrive at the specified time.  The result: preference over the delivery schedule is NOT a monotonically increasing or decreasing function over the time. Assumption for the Algorithm

9 Weeks (or days) Utility for seller for buyer Delivery Schedule as the Example The preference from the buyer’s side is 2, 6, 1, 5, 4, and 3. The preference from the seller’s side is 3, 4, 2, 1, 5, and 6.

10 Algorithm to Conduct Bilateral Negotiation Only Pareto Optimal Solutions are left after the first algorithm has been applied. For each negotiator, each solution is assigned a “counting number”. Negotiators exchange proposals/counterproposals by consulting the pre-assigned “counting number.”

11 X: Number of Proposal Exchange Y: Ordered Preference List of Alternatives P 11 P 12 P 21 P 22 No-Decreasing Curve for N 1 No-Increasing Curve for N 2 PaPa xaxa yaya Proof of Guaranteed Termination

12 Service Provider Service Requestor 1.Visa [3] 2.MC [2] 3.AMEX [5] 4.DC[4] 1.DC [2] 2.MC [3] 3.AMEX [4] 4.Visa[5] Visa (1) DC (1) Visa (2) DC (2) Visa (3) MC (1) Accept MC Credit Card as Private Information

13 Conclusion Two algorithms have been presented. Application of these two algorithms in information privacy. Credit card example may not be very convincing. Algorithms are general enough to be used in other domains.