1. Scalable and E ffi cient Reasoning for Enforcing Role-Based Access Control Tyrone Cadenhead Murat Kantarcioglu, and Bhavani Thuraisingham 1

2. Overview  Motivation  Contributions  Approach  Theoretical Background: –RBAC, TRBAC, Description Logics, SWRL  Detailed Overview of Approach and Optimizations  Example  Experimental Results 2

3. Motivation  Organizations tend to generate large amount of data (or resources)  Users need only partial access to resources  Pairs: (user, role) (role, permission) (action, resource)  n u users and n r roles  at most n u ×n r mappings  Scalable access control model  Exchange expertise among experts, between systems  Heterogeneity in system  Make decision with data  Formal Semantics of Data 3

4. Motivation (cont’d)  RBAC simplifies Security Management –But Roles are statically defined  TRBAC extends RBAC –Roles are dynamically defined and have a temporal dimension –Does not address Heterogeneity inherent in organization information systems  Ontology has a Common Vocabulary –Conforms to a Description Logic (DL) formalism Description Logic (DL) Reasoning Service –Can be Distributed as over a set of Knowledge Bases 4

5. Why Flexible RBAC SamBobPhysician Sam allowed access to Bob record –When Bob is under is care SamKellyEmergency: Sam is off duty, Kelly in emergency room: –Bob –Bob needs immediate treatment –KellyBob –Kelly not pre-assigned to view/update Bob’s record  Temporal RBAC 5

6. Why Flexible TRBAC  Kelly  Kelly needs to collaborate with different specialist from different expertise  Sharing of data across wards, departments  Seamless and unambiguous exchange of information  Ontologies  Common Vocabulary  Enable reconciliation and translation between different standards 6

7. Automation  Kell  Kelly and team make decisions  Using Bob medical history  Access is needed Temporarily  Accuracy and efficiency critical  Automated Tool  Access granted in Emergency session  Apply policy rules over relevant data in Bob’s record  Verify the decisions based on formal logic  Make access decisions efficiently 7

8. Main Contributions  TRBAC Implementation using existing semantic technologies  Reasoning Service for access control over large numbers of data instances in DL Knowledge Bases (KBs)  E ffi ciently and accurately reason about access rights 8

9. Approach  Transform temporal access control policies to rules :  Semantic web rule language (SWRL)  Partitioning the Knowledge Base (KB)  - Terminological Box (TBox)  - Assertional Box (ABox)  A Knowledge Base consists of a TBox and ABox 9

10. Approach (cont’d)  Achieves: 1. Scalability – support many users, roles, sessions, permissions; combinations w.r.t access control policies 2. E ffi ciency - determines the response time to make a decision in milliseconds 3. Correct reasoning – ensure all data assertions available when applying the security policies 10

11. Theoretical Background RBAC TRBAC Description Logic Language (ALCQ) SWRL 11

12. RBAC 12

13. (Mappings) Connect individuals from two domain modules:  RBAC assignments: Think of mappings as relations of form P(i, j) with valid pairs (i, j) user-role, role-user, role-permission, permission-role, session- user, role-role and session-role a binary relationship of form P(x, y), a restriction on values assigned to (x, y) pairs  Hospital extensions: the mappings patient-user, user-patient and patient-session  Patient-Record constraint: the one-to-one mappings patient-record and record-patient 13

14. TRBAC  Extension of RBAC  Supports temporal access  Expressed by means of role triggers  Constrains the set of roles that a particular user can activate at a given time instant  Triggers  Firing a trigger cause a role to be enabled/disabled  Conflict Resolution  Simultaneous enabling and disabling of a role  Priorities 14

15. Description Logics Formally build our domain concepts and the relationships between them. Add semantics (reasoning) Use a knowledge representation language We can formally say a doctor is a user, a surgeon is a doctor, a doctor has a medical degree. 15

16. Description Logics 16

17. SWRL Semantic Web Rule language (SWRL) W3C recommendation. A SWRL rule has the form: h i, b j are atoms of the form C(x), P(x, y), sameAs(x,y), or differentFrom(x,y), where C is an OWL description, P is an OWL property, and x, y are Datalog variables, OWL individuals, or OWL data values 17

18. Overview 18

19. Intuition a user assigned to role : –User attributes (name, sex, id) in partition –Details relating to role in partition –Session related details in partition Query : Optimization: 19

20. Step 1 Build step offline Restrict each partition size: ensures each KB fits into the memory on the machine 20

21. Step 2 Load the policy rules into a new knowledge base. –Rules determine which assertions are relevant to determine any policy objective. Adding rules to more efficient Experimental results: –Impact on the reasoning time vs. adding rules to –Rules apply to a small subset of triples –Reduced number of symbols in the ABox 21

22. Step 3 RBAC: 22

23. Inference Stage When there is an access request for a specific patient, start executing steps 2 and 3. Steps 2 and 3 are our inferencing stages where we enforce the security policies. These can also be executed concurrently for many patients, as desired. 23

24. TBox RBAC: –The sets and are atomic concepts in –Mappings and are formalized as DL roles Employees are Users Primary Physicians are employees with at least one patient We can Conclude primary physicians are users. 24

25. ABox 25

26. RDF W3C recommendation Make assertions about any resources on the semantic Web We can say Bob is a doctor –Doctor(Bob)  (Bob rdf:type Doctor) Bob attended Harvard –(Bob, attended, “Harvard”) 26

27. Distributed Reasoning 27

28. Home Partition 28

29. Connecting Partitions 29

30. Distributed Reasoning Physicians can be both a primary or emergency-room physician, and restricted to two roles. Verify Bob does not exceed two roles – –Execute query over is sufficient Primary Physicians attend to at most five patients at a time –Query each one at a time is sufficient 30

31. Temporal RBAC Reasoning Implement TRBAC as triggers –TBox –ABox 31

32. Temporal RBAC Reasoning Periodic Event Trigger: –doctor-on-day-duty must be enabled during the night –nurse-on-night-duty must be enabled whenever the role doctor-on- night-duty is 32

33. Advantages 33

34. Optimization  Two types of indexing: 1.indexing the assertions Allow finding triple by subject (s), a predicate (p) or an object (o), without the cost of a linear search over all the triples in a partition 2. creating a high level index. points to the location of the partitions on disk At most linear with respect to the number of partitions 34

35. Policy Query 35

36. Example 36

37. Trace 37

38. Experiments 38

39. Experiments 39