Managing the Assured Information Sharing Lifecycle

1. Managing the Assured Information Sharing Lifecycle Tim FininUMBC 22 June 2009 http://aisl.umbc.edu/show/resource/id/504/

2. 2008 MURI project University of Maryland, Baltimore County (Lead Inst.) T. Finin (Lead), A. Joshi, H. Kargupta, A. Sherman, Y. Yesha Purdue University E. Bertino (Lead), N. Li, C. Clifton, E. Spafford University of Texas at Dallas B. Thuraisingham (Lead), M. Kantarcioglu, L. Khan, A. Bensoussan, N. Berg University of Illinois at Urbana Champaign J. Han (Lead), C. Zhai University of Texas at San Antonio R. Sandhu (Lead), J. Massaro, S. Xu University of Michigan L. Adamic (Lead) Summer 2008 start

3. 9/11 and related events illustrated problems in managing sensitive information Managing Web information & services with appropriate security, privacy and simplicity is increasingly important and challenging Autonomous devices (mobile phones, rout-ers & medical equipment) need to share, too Moving to EMRs is a national goal, but raises many privacy issues Business needs better models for DRM Motivation for AIS

4. Misunderstanding Policy Here’s a conclusion from a study prepared in 2004 for the 9/11 Commission “The information sharing failures in the summer of 2001 were not the result of legal barriers but of the failure of individuals to understand that the barriers did not apply to the facts at hand. Simply put, there was no legal reason why the informa-tion could not have been shared.” Legal Barriers to Information Sharing: The Erection of a Wall Between Intelligence and Law Enforcement Investigations, Commission on Terrorist Attacks Upon the US, Staff Mono-graph, Barbara Grewe, Senior Counsel for Special Projects, 20 Aug. 2004.

7. Our research themes • An information value chain of producers & con-sumers yields an assured information sharing lifecycle • Policies for trust, access and use grounded in sharable semantic models operating in a service oriented architectureaccelerate sharing • Newintegration and discoverytechniques are required to assure information quality and privacy • Modeling, analyzing and exploiting social networksand incentives for sharing

8. Information value chain

9. web Information value chain Potentially, everyone is both an information consumer and producer

10. web Information value chain The advertizing/discovery process must be controlled to prevent inappropriate disclosure A system discovers information it can use from the advertisements of others

11. web Information value chain Negotiation involves exchange of credentials& certificates, producing permis- sions & obligations The principles negotiate a policy for the information’s acquisition and use

12. web Information value chain We must assure correct semantics and information quality The information is used, often resulting in the discovery of new knowledge

13. web Information value chain Enforce obligations on usage and re-sharing, privacy-preserving summaries, incentives for sharing which is screened, adapted and summarized for possible release

14. web Information value chain Incentives encourage offering to share information and appropriately characterized in advertisements for others to find

15. Sample of AISL Recent Results • New models, architectures, languages & mechanisms for trustworthiness-centric AIS (UTSA, Purdue) • EXAM: environment for XACML policy analysis and management (Purdue) • Techniques for resolving conflicting facts extracted from different resources (UIUC, Purdue) • Information sharing motivation and incentives (UTD, Michigan) • Inferring access policies from logs (UMBC) • Privacy policies in mobile/social information systems (UMBC) • AIS infrastructure (ALL)

16. 1 Trustworthiness-centric AIS Framework • Objective: create a trustworthiness-centric assured information sharing framework • Approach: design models, architectures, lang-uages and mechanisms to realize it • Key challenges, management for: • Trustworthiness and riskfor end-user decision making • Usage, extending simple access control • Attacks, including trustworthiness of infrastructure services • Identityextending current generation • Provenance for managing trustworthiness of data, software, and requests

17. 1 Group-Centric Secure Info Sharing Dissemination-Centric • Traditional model • Attributes & policies attached to objects (“sticky policies”) • Policies enforced as objects disseminated from producer to consumer Group Centric • New model • Objects & subjects brought together as a group for sharing • Simultaneous co-presence for access • Two metaphors: se-cure meeting room; subscription service

18. 1 Progress on g-SIS • Developed a formal model for a g-SIS system using linear temporal logic (LTL) • e.g., events for subjects (join, leave) and objects (add, remove), requests (read), Authz(s,o,r), … • Specify core properties g-SIS must satisfy • e.g, Simultaneity, Provenance, Persistence, Availability, … • Specify additional group operator properties • Prove specifications satisfy correct author-ization behavior using model checker • See SACMAT 2009 paper

19. 2 EXAM • The management and consolidation of a large number of policies can be an impediment to AIS • EXAM is a prototype system for policy analysis and management, which can be used for • policy property analyses • policy similarity analysis • policy integration • Focus on access control policies in XACML (Extensible Access Control Markup Language) • Analyzer combines advantages of existing MTBDD-based and SAT-solver-based techniques MTBDD = Multi-Terminal Binary Decision Diagram

20. 2 Policy Similarity Analysis PSA Query : Find all requests permitted by both policies. Disjoint predicates : time cannot have two different values in any request. Both policies permit download action when membership type is monthly and time < 19:00 No access is permitted by both policies for video files between 20:00 and 21:00. Both policies permit download action to monthly subscribers between 21:00 and 22:00 only if the content type is not video.

21. 2 EXAM - PSA Example Both policies permit download of video files to monthly memberships if time is less than 19:00 or time is between 22:00 and 23:45. This example considers the case where membership can be both weekly and monthly. Demonstrated at SACMAT 2009

22. Truth Discovery with MultipleConflicting Information Providers Heuristic Rule 2: A web site providing mostly true facts for many objects will likely pro-vide true facts for others Problem: Multiple information providers provide conflicting facts for same object e.g.: given different author names for a book, which isthe true author? Web sites Facts Objects w1 f1 o1 f2 w2 f3 w3 f4 o2 w4 f5 3 Heuristic Rule 1: The false facts on different web sites are less likely to be the same or similar. False facts are often introduced by random factors

23. Multi-version of truth Democrats vs. republicans may have different views Truth may change with time A player may win first but then lose Truth is a relative, dynamically changing judgment Incremental updates with recent data in data streams Method: Veracity-Stream Dynamic information network mining for veracity analysis in multiple data streams Current Testing Data Sets Google News: A dynamic news feed that provides functions and facilitates searching and browsing 4,500 news sources updated continuously 3 Truth-Discovery: Framework Extension Common semantic model helps here

24. Knowledge iN 4 Motivation & quality in information sharing • Analyzed online Q&A forums: 2.6Mquestions, 4.6M answers and interviewswith 26 top answerers • Motivations to contribute include: altruism,learning, competition (via point system) andas a hobby • Users who contribute more often and lessintermittently contribute higher qualityinformation • Users prefer to answer unansweredquestions and to respond to incorrectanswers • Useful knowledge for designing betterincentive systems to encourage information sharing

25. 4 AIS with coalitional partners: incentives & trust • Combining intelligence through a loose alliance • Bridge gaps due to sovereign boundaries • Maximize yield of resources • Discover new information via correlation, analysis of the ‘big picture’ • Information exchanged privately between two participants • Drawbacks to sharing: Misinformation and Freeloading • Goal: Create means of encouraging desirable behavior within an environment which lacks or cannot support a central governing agent Motivation • Approach:evolutionary game theoretic framework to see effects of trust and incentives in truthful information sharing • Results: truth telling emerges as dominant strategy with enough agents that punish untruthful behavior • See: Layfield et al., CollaborateComm ’08

26. 4 Social Network Security and Privacy • Social Networks are important for AIS • Social links affect how the information is shared • Social network security and privacy becomes important • Goals for online social media systems: • Create flexible and efficient access control systems • Explore privacy disclosure issues Motivation • Semantic web access control systems for social networks • Social network is modeled using OWL ontologies. • Access control policies represented in SWRL rules • See SACMAT 09 paper for details • .Preventing privacy disclosures on online social networks • Use data mining to choose best attributes and links to delete to prevent private information disclosures. • See WWW ’09 paper for details Approach

27. 5 Inferring RBAC Policies • Problem: A system whose access policy is known is more vulnerable to attacks and insider threat Attackers may infer likely policies fromaccess observations, partial knowledgeof subject attributes, and backgroundknowledge • Objective: Strengthen policiesagainst discovery • Approach: Explore techniques topropose policy theories via machinelearning, including ILP and SVMs • Results: promisinginitial results forsimple Role Based Access Control policies

28. 6 Privacy policies for mobile computing Policies compiled to RDF N3 rules # Share location with teachers 9-6 weekdaysif on campus { REQ a rein:Request REQ rein:resource LOCATION. ?T a TeachersGroupStuff. ?R a UserStuff; log:include { LOCATION a tu:Location; USERID a tu:Userid }. REQ rein:requester WHO. ?T a TeachersGroupStuff; log:includes { [] t:member [ session:login USERID ] }. LOCATION loc:equalTo :UMBC . WHO :requestTime ?time. "" time:localtime ?localTime. ?localTime time:dayOfWeek ?day. ?day math:notlessthan "1". ?day math:notgreaterthan "5". ?localTime time:hour ?dtime. ?dtime math:notlessthan "9". ?dtime math:notgreaterthan "18". } => { WHO loc:can-get LOCATION }. • Problem: mobile devices collect and integrate sensitive private data about their users which they would like to selectively share with others • Objective: Develop a policy-based system for information sharing with an interface enabling end users to write & adapt privacy policies • Approach: prototype component foriConnect on an iPhone and evaluate ina University environment • Example policy rules: share my exactlocation with my family; share currentactivity with my close friends, … AIR reasoner • We use MIT’s AIR reasoner for N3 rules • This produces conclusions as well as justifications for each • The justifications are used to explain the policy results

29. 7 AIS Service Oriented Architecture • An event-based model allowscomponents to share context • Shared semantic models fordescriptions, communicationand policies • Initial prototype uses ApacheAxis2 SOA Framework • Host policy tools as services • TODO: add enhanced agent-based protocols for advertising, negotiation and argumentation service calls & interactions discovery release use semantic events

30. Papers, dissertations and theses • Over 50 refereed papers published/accepted • Three PhDs completed • Deng Cai, Ph.D., Illinois, Spectral regression: a regression framework for efficient regularized subspace learning,May 2009 • Kamalika Das, Ph.D., UMBC, Game-theoretic approach toward privacy preserving distributed data mining, August 2009 • Xiaolin Shi, Ph.D., Michigan, The Structure and Dynamics of Information Sharing Networks, June 2009 • Two MS degrees completed • Kishor Datar, M.S., UMBC, Reverse Engineering of RBAC Policy using Access Logs, June 2009 • Audumbar Chormale, M.S., UMBC, Policies based framework to constrain information flow in social networks, July 2009 • Available at http://aisl.umbc.edu/

31. Some Other Highlights • Lada Adamic (Mich) co-authored paper in Science: Computational Social Science (2009) • Jiawei Han (Illinois) elected IEEE Fellow (2008) • Ravi Sandhu (UTSA) elected AAAS Fellow (2008); received ACM SIG on Security, Audit & Control Outstanding Contributions Award (2008) • Tim Finin (UMBC) IEEE Technical Achievement Award (2009) • Bhavani Thuraisingham (UTD) chaired 2009 IEEE Intelligence and Security Informatics Conf. • ChengXiang Zhai (UIUC) co-chaired ACM SIGIR 2009 • Hillol Kargupta co-chairs 2009 IEEE Data Mining Conf. • Tim Finin (UMBC) chaired 2008 Int. Semantic Web Conf.

32. Conclusions • Assured information sharing in open, heter-ogeneous, distributed environments is increasingly important • New policy frameworks and languages can help • Semantic Web technologies share common policy concepts, policies & domain models • Data quality and privacy-preserving tech-niques must be addressed • Social aspects are important: networks, incentives

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