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XML, RDF, Workflow Security

XML, RDF, Workflow Security. Reading. Required:

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XML, RDF, Workflow Security

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  1. XML, RDF, Workflow Security Computer Science and Engineering

  2. Reading • Required: • Ernesto Damiani, Sabrina De Capitani di Vimercati, Stefano Paraboschi, and Pierangela Samarati. 2002. A fine-grained access control system for XML documents. ACM Trans. Inf. Syst. Secur. 5, 2 (May 2002), 169-202.  http://dl.acm.org/citation.cfm?id=505590 • A. Stoica and C. Farkas, “Secure XML Views,” Proc. 16th IFIP WG11.3 Working Conference on Database and Application Security, 133-146, 2002. http://www.cse.sc.edu/~farkas/publications/c5.pdf • Amit Jain and Csilla Farkas. 2006. Secure resource description framework: an access control model. In Proceedings of the eleventh ACM symposium on Access control models and technologies (SACMAT '06). ACM, New York, NY, USA, 121-129., http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.84.792&rep=rep1&type=pdf Computer Science and Engineering

  3. Semantic Web From: T.B. Lee

  4. Secure Technologies • Security on the Web • Data Security • XML • Inferences • Metadata Security • RDF • Application Security

  5. Secure XML Views - Example medicalFiles <medicalFiles> UC <countyRec> S <patient> S <name>John Smith </name> UC <phone>111-2222</phone> S </patient> <physician>Jim Dale </physician> UC </countyRec> <milBaseRec> TS <patient> S <name>Harry Green</name> UC <phone>333-4444</phone> S </patient> <physician>Joe White </physician> UC <milTag>MT78</milTag> TS </milBaseRec> </medicalFiles> countyRec milBaseRec physician Jim Dale physician Joe White milTag MT78 patient patient name John Smith phone 111-2222 name Harry Green phone 333-4444 View over UC data

  6. Secure XML Views - Example cont. medicalFiles <medicalFiles> <countyRec> <patient> <name>John Smith</name> </patient> <physician>Jim Dale</physician> </countyRec> <milBaseRec> <patient> <name>Harry Green</name> </patient> <physician>Joe White</physician> </milBaseRec> </medicalFiles> countyRec milBaseRec physician Jim Dale physician Joe White patient patient name John Smith name Harry Green View over UC data

  7. Secure XML Views - Example cont. medicalFiles <medicalFiles> <tag01> <tag02> <name>John Smith</name> </tag02> <physician>Jim Dale</physician> </tag01> <tag03> <tag02> <name>Harry Green</name> </tag02> <physician>Joe White</physician> </tag03> </medicalFiles> countyRec milBaseRec physician Jim Dale physician Joe White patient patient name John Smith name Harry Green View over UC data

  8. Secure XML Views - Example cont. medicalFiles <medicalFiles> UC <countyRec> S <patient> S <name>John Smith</name> UC </patient> <physician>Jim Dale</physician> UC </countyRec> <milBaseRec> TS <patient> S <name>Harry Green</name> UC </patient> <physician>Joe White</physician> UC </milBaseRec> </medicalFiles> countyRec milBaseRec physician Jim Dale physician Joe White patient patient name John Smith name Harry Green View over UC data

  9. Secure XML Views - Example cont. medicalFiles <medicalFiles> <name>John Smith</name> <physician>Jim Dale</physician> <name>Harry Green</name> <physician>Joe White</physician> </medicalFiles> physician Jim Dale name John Smith physician Joe White name Harry Green View over UC data

  10. Secure XML Views - Solution • Multi-Plane DTD Graph (MPG) • Minimal Semantic Conflict Graph (association preservation) • Cover story • Transformation rules

  11. Multi-Plane DTD Graph <milBaseRec> D,medicalFiles UC <milTag> TopSecret S TS D, countyRec D, milBaseRec <countyRec> UC S TS D, physician <patient> D, patient D, milTag Secret <phone> UC S D, name D, phone <physician> <name> <medicalFiles> Unclassified MPG = DTD graph over multiple security planes

  12. Transformation - Example <milBaseRec> MPG <milTag> TS MSCG <countyRec> <patient> name phone S <phone> physician <medicalFiles> Security Space Secret UC <physician> <name>

  13. Transformation - Example <milBaseRec> <milTag> TS <countyRec> <patient> name S <phone> physician <emrgRec> <medicalFiles> MSCG UC <physician> <name> SP MPG

  14. Transformation - Example <milBaseRec> <milTag> TS <countyRec> <patient> S <phone>  <emrgRec> <medicalFiles> MSCG UC <physician> <name> SP MPG

  15. Transformation - Example <milBaseRec> <milTag> TS medicalFiles <countyRec> <patient> emergencyRec S <phone> physician <emrgRec> name <medicalFiles> UC <physician> <name> SP Data Structure MPG

  16. MedicalDb SSN Patient * Name Name Patient Phone Phone Birthdate Race * Diagnosis Date Patient Physician Prescription Comments Birthdate Allergies * Race Allergen Diagnosis Date Comments Node Association - Example DTD of Patient Health Record

  17. Object - Association level classification Node level classification + - + + + + Layered Access Control

  18. t1 t2 t3 t4 Simple Security Object o  ti :(ti) = (o)

  19. t1 t2 t3 t4 Association Security Object o  ti : (ti) < (o)

  20. // r d a b c v1 v1 Query Pattern FOR $x in //r LET $y := $x/d, $z := $x/a RETURN <answer> {$z/c} </answer> WHERE { $z/b==$y} Query Pattern

  21. Pattern Automata • Pattern Automata X = { S, Q, q0 , Qf , d } • S = E  A  { pcdata, //} • d is a transition function • Q = {q0 , … , qn} • Qf Q, (q0 Ï Qf) • Valid transitions on d are of the following form: s(qi, … ,qj)  qk • If d does not contain a valid transition rule, the default new state is q0

  22. // a b c Pattern Automata - Example • = { a, b, c, //} Q = {q0, qa, qb, qc} Qf = {qa} d= { b( )  qb , c( )  qc , a(qb,qc)  qa , *(qa)  qa } Association object Pattern Automata

  23. The Inference Problem General Purpose Database: Non-confidential data + Metadata Undesired Inferences Semantic Web: Non-confidential data + Metadata (data and application semantics) + Computational Power + Connectivity  Undesired Inferences

  24. Air show address fort address fort Association Graph • Association similarity measure • Distance of each node from the association root • Difference of the distance of the nodes from the association root • Complexity of the sub-trees originating at nodes • Example: XML document: Association Graph: Public Public, AC

  25. Public Public ? address fort Water source base district basin Confidential Correlated Inference Concept Generalization: weighted concepts, concept abstraction level, range of allowed abstractions Object[]. waterSource :: Object basin :: waterSource place :: Object district :: place address :: place base :: Object fort :: base

  26. place address fort Water source district basin base Confidential Correlated Inference (cont.) Object[]. waterSource :: Object basin :: waterSource place :: Object district :: place address :: place base :: Object fort :: base Base Place base Public Public Water source Water Source

  27. Inference Removal • Relational databases: limit access to data • Web inferences • Cannot redesign public data outside of protection domain • Cannot modify/refuse answer to already published web page • Protection Options: • Release misleading information • Remove information • Control access to metadata

  28. Metadata Security • No security model exists for metadata • Can we use existing security models to protect metadata? • RDF/S is the Basic Framework for SW • RDF/S supports simple inferences • This is not true of XML: XML Access control cannot be used to protect RDF /S data

  29. RDF/S Entailment Rules Example RDF/S Entailment Rules (http://www.w3.org/TR/rdf-mt/#rules ) • Rdfs2: • (aaa, rdfs:domain, xxx) + (uuu, aaa, yyy)  (uuu, rdf:type, xxx) • Rdfs3: • (aaa, rdfs:range, xxx) + (uuu, aaa, vvv) (vvv, rdf:type, xxx) • Rdfs5: • (uuu, rdfs:subPropertyOf, vvv) + (vvv, rdfs:subPropertyOf, xxx) (uuu,rdfs:subPropertyOf, xxx) • Rdfs11: • (uuu, rdfs:subClassOf, vvv)+(vvv, rdfs:subClassOf, xxx)(uuu,rdfs:subClassOf, xxx)

  30. Example Graph Format • RDF Triples: • (Student, rdfs:subClassOf, Person) • (University, rdfs:subClassOf, GovAgency) • (studiesAt, rdfs:domain, Student) • (studiesAt, rdfs:range,University) • (studiesAt, rdfs:subPropertyOf, memberAt) • (John, studiesAt, USC)

  31. Example Graph Format

  32. Example Graph Format

  33. Example Graph Format

  34. Secure RDF Entailed Data in RDF can cause illegal inferences: • (John, studiesAt, USC) [S] + (studiesAt, rdfs:domain, University) [S]  (USC, rdf:type, University) [S] • (USC, rdf:type, University) [S]+ (University, rdf:subclassOf, GovAgency) [S]  (USC, rdf:type, GovAgency) [TS] Secret User can infer TS information

  35. RDF Access Control • Security Policy • Subject • Object – Object pattern • Access Mode • Default policy • Conflict Resolution • Classification of entailed data • Flexible granularity

  36. Business Process • Increased complexity • Workflow specification • Workflow correctness • Workflow security • Automated analysis Internet Security - Farkas

  37. Workflow Verification • Detect conflicts and anomalies • Lack of formal methods and tools Internet Security - Farkas

  38. What to represent? • Activity-based workflow model • Design-time analysis • Implementation-time verification • Reading: propositional logic • Activities • Basic workflow constructs • Activity “leads” to other activity Internet Security - Farkas

  39. Workflow a2 a1 + a4 Internet Security - Farkas

  40. WS-BPEL • Language to specify business processes that are composed of Web services as well as exposed as Web services • WS-BPEL specifications are portable -- can be carried out by every WS-BPEL compliant execution environment Internet Security - Farkas

  41. Two-Level Programming Model • Programming in the large • Non-programmers implementing processes • Flow logic • Programming in the small • Programmers implementing low-level services • Function logic Internet Security - Farkas

  42. WS-BPEL Flow Oriented • Request • Invoke • Response • SOA and WS-BPEL Internet Security - Farkas

  43. Security and Workflow • Identity Management • Authorization: e.g., data access controls • Process constraints • Provenance Internet Security - Farkas

  44. Issues • Need to distinguish between functionality & security guarantees • How to handle trust management? • Workflows are process or data centric • How to map to user-centric system security policies? • Planning and enactment are complex/rich processes • How to establish security assurance of a complex mechanism? Internet Security - Farkas

  45. Next Class • Cloud computing Computer Science and Engineering

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