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1、Assured Information SharingProf. Bhavani Thuraisingham and Prof. Latifur KhanThe University of Texas at DallasProf. Ravi SandhuGeorge Mason UniversityAugust 2006Information Operation Across Infospheres: Assured Information SharingAcknowledgements StudentsUTDallas Dilsad Cavus (MS, Data mining and da

2、ta sharing) Srinivasan Iyer (MS, Trust management) Ryan Layfield (PhD, Game theory) Mehdi (PhD, Worm detection)GMU Min (PhD, Extended RBAC) Faculty and StaffUTDallas Prof. Murat (Game theory) Dr. Mamoun Awad (Data mining and Data sharing) Project supplemented by Texas Enterprise FundsArchitectureExp

3、ortData/PolicyComponentData/Policy for Agency AData/Policy for FederationExportData/PolicyComponentData/Policy for Agency CComponentData/Policy for Agency BExportData/PolicyOur Approach Integrate the Medicaid claims data and mine the data; next enforce policies and determine how much information has

4、 been lost by enforcing policies Examine RBAC and UCON in a coalition environment Apply game theory and probing techniques to extract information from non cooperative partners; conduct information operations and determine the actions of an untrustworthy partner. Defensive and offensive operationsCoa

5、lition for Assured Information SharingA coalition is formed by related entities like Medical care facilities and Health insurance companies to share information within themselves and to selected outsiders in a secure manner.Each of these entities can specify policies for access control through the c

6、oalition.The coalition provides controlled access to information in the database after enforcing policies generated by the entity owning the database.ExampleConsider a set of hospitals: H1, H2, Hn and a set of Health Insurance agencies I1, I2, In. Each of these hospitals and insurance companies can

7、be an entity in a Coalition.The Coalition server provides a single point of access for databases owned by each of the member entities.Each of these entities provides a policy file to the company/individual hosting the Coalition server.Example continuedSituation 1When registering a client, an insuran

8、ce agency I5 might want to ensure that the applicant does not have an active insurance coverage from any other insurance agency.A web service agent from the insurance company I5 issues a request to the Coalition server to verify this. - Using the applicants SSN, the web service agent can query each

9、of the other insurance agency databases to retrieve records of active insurance coverage for the client with the specified SSN.CoalitionArchitecture Architectural Elements 1Web Service Agent: Formulates a request and sends the request to the Response Engine Receives response from the Response Engine

10、 and uses it to provide the appropriate service.The aim of the web service agent is to obtain as much information as possible.Architectural Elements 2Response Engine:Policy Enforcement Point (PEP): Enforces policies on requests sent by the Web Service.Translates this request into an XACML request; s

11、ends it to the PDP.Policy Decision Point (PDP): Makes decisions regarding the request made by the web service.Conveys the XACML request to the PEP.Architectural Elements 3Coalition ( Policy files + Database)Policy Files: Policy Files are written in XACML policy language. Policy Files specify rules f

12、or “Targets”. Each target is composed of 3 components: Subject, Resource and Action; each target is identified uniquely by its components taken together. The XACML request generated by the PEP contains the target. The PDPs decision making capability lies in matching the target in the request file wi

13、th the target in the policy file. These policy files are supplied by the owner of the databases (Entities in the coalition).Databases: The entities participating in the coalition provide access to their databases.Screenshots - 1Screenshots - 2Screenshots - 3Enforcing Honesty Everyone has a choice: T

14、ell the truth Lie Distributed Behavior Enforcement Non-trivial to implement Difficult to guarantee Examples: BitTorrent, P2P Networks, etc. Unless we can afford to have a neutral 3rd party that everyone can agree on, we need some way of enforcing good behaviorPunishment However, there is a third opt

15、ion: refuse to participate Usually not researched Drastic measure that only makes sense if we can influence behavior Our modeling suggests that, with proper use of refusal, we can ultimately enforce helpful behavior without a managing agent Evolutionary Strategy Every 200 rounds, we create a new gen

16、eration of agents, using the most successful strategies available The fitness f() of a given agent is a function of how well they have performed during interaction with other agents More successful agents have a higher probability of being a part of the next generation=niiiiselectafafap0)()()(Our Wo

17、rk Our mathematical models suggest that, assuming we punish by cutting off communication, the equilibrium is to always tell the truth Therefore, using an evolutionary environment, we have placed our particular rationality amongst a heterogeneous pool of competing ideologies Tit-For-Tat: A famous alg

18、orithm that simply mirrors the last move an opponent made Random: An agent that selects its strategy with a 50/50 chance Casual Liar: Like our agent, but lies with a 10% probability Subtle Liar: Like our agent, but chooses to lie when it perceives the piece being traded is of significant value With

19、equal parts given to each agent, which one will emerge victorious?ResultsCentralized Reputations in Decentralized P2P NetworksNathalie Tsybulnik, Kevin W. Hamlen, Bhavani ThuraisinghamMotivation P2P Systems offer few security guarantees Shared data has low confidentiality Shared data has low integrity Easy for malicious peers to propagate malicious codeI