E-Privacy – Privacy in the Electronic Society

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E-Privacy – Privacy in the Electronic Society

  1. 1. E-Privacy – Privacy in the Electronic Society Privacy Policy Languages Enterprise Privacy Architectures Günter Karjoth Spring term 2010 1 Dienstag, 23. März 2010
  2. 2. Potential semantic inconsistencies in P3P policies  multiple retention values apply to one data item;  a statement has conflicting purposes and retention values;  purpose historical with retention no-retention  a statement has conflicting purposes and recipients;  all purposes but only recipient delivery  a statement has conflicting purposes and data items  purpose contact but no data items physical or online [YuLiAn04] 2 Dienstag, 23. März 2010
  3. 3. What is the difference ? Policy 1: Policy 3: stmt( data: {#user.home-info.telecom, stmt( data: {#user.home-info.telecom}, #user.bdate(optional)}, purpose: {individual-analysis, purpose: {individual-analysis, telemarketing(opt-in)}, telemarketing(opt-in)}, recipient: {ours}, recipient: {ours}, retention: {stated-purpose} ) retention: {stated-purpose} ) stmt( data: {#user.bdate(optional)}, purpose: {individual-analysis, Policy 2: telemarketing(opt-in)}, stmt( data: {#user.home-info.telecom, recipient: {ours}, #user.bdate(optional)}, retention: {stated-purpose} ) purpose: {individual-analysis}, recipient: {ours}, retention: {stated-purpose}) stmt( data: {#user.home-info.telecom, #user.bdate(optional)}, purpose: {telemarketing(opt-in)}, recipient: {ours}, retention: {stated-purpose} ) 3 Dienstag, 23. März 2010
  4. 4. P3P Base Data Structures home-info •user •user.home-info telemarketing •user.home-info.postal •user.home-info.postal.name contact (but not telemarketing) •user.home-info.postal.name.family 4 Dienstag, 23. März 2010
  5. 5. Modelling P3P Policies <STATEMENT> <PURPOSE><current/><contact required="opt-in"/> </PURPOSE> <RECIPIENT><ours/></RECIPIENT> <RETENTION><business-practices/></RETENTION> <DATA-GROUP> <DATA ref="#customer.home-info.postal"/> <DATA ref="#dynamic.miscdata"/> <CATEGORIES><demographic/><physical/></CATEGORIES> </DATA> </DATA-GROUP> </STATEMENT>  A P3P statement r can be interpreted as a function P[r], from a data hierarchy D to subsets of actions 2A.  P3P has a union semantics: P[p]d = Ur∈p P[r]d because it permits an action if permitted by at least one statement.  How can you verify that a service provider has promised not to perform action a on data d ? 5 Dienstag, 23. März 2010
  6. 6. The model  A – set of all potential actions (including purpose, recipient, retention, etc.)  D – set of data objects, ordered by ≤D, such that if d1 ≤D d2 then d1 is a component of d2.  A privacy policy, p, is a function from D to subsets of A: p(d) ⊆ 2A  a service provider who performs action a on data d has violated p if a ∉ p(d)  a ∈ p(d) does not imply action a may be performed on data d because the provider effectively performs a on all d’ ≤D d 6 Dienstag, 23. März 2010
  7. 7. Perspectives and Modalities  Consumers want privacy policies that permit as little use of their personal data as possible.  use an upper bound in case of incomplete or conflicting information  - ability to perform an action using some parts of data  Service providers want privacy policies that permit as much use of their customers’ personal data as is useful.  use an lower bound  ☐ - ability to perform an action using all parts of data [Barth&Mitchell, 2005] 7 Dienstag, 23. März 2010
  8. 8. Perspectives and Modalities (cont’d) Notice that a single detailed policy allowing some but not all data be used for telemarketing, • a privacy-conscious consumer would like the policy be summarized (using the modality) as “allows telemarketing” so the consumer can reject the policy, whereas • a service provider who has committed to the policy must summarize the policy (using the ☐ modality) as “prohibits telemarketing” in order to avoid violating the policy when it is enforced using the policy summary. ps ≤ p ≤ pc The ≤ relation captures the intuitive notion that one policy is less permissive than another, if the former makes a stronger promise, for every data object, than the latter. 8 Dienstag, 23. März 2010
  9. 9. Kripke model The Kripke model for policy p is (D, ≤D , and ⊫p ), where for all d ∈ D and all a ∈ A, d ⊫p a ⇔ a ∈ p(d) . We extend the ⊧ relation to modal formulae in the standard manner: d ⊫p φ1 ∧ φ2 ⇔ d ⊫p φ1 and d ⊫p φ2 d ⊫p ¬ φ ⇔ d ⊯p φ d ⊫p ☐ φ ⇔ (∀d’ ≤D d) (d’ ⊫p φ) and adopt the convention that φ ≡ ¬☐¬φ. 9 Dienstag, 23. März 2010
  10. 10. Enforcement Intuitively, one policy entails another if the former policy makes a stronger promise! Given privacy policies p and q with a common data hierarchy D, p entails q, written p ≤ q, if for all d ∈ D, p(d) ⊆ q(d) Intuitively, one policy enforces another if any action performed under the former is announced under the latter. Given policies p and q with data hierarchy Dp and Dq, respectively, and Dp ≤ Dq, q enforces p if and only if d ⊫q φ ⇒ d ⊫p φ for all d ∈ Dp and all positive simple, modal formulae φ. 10 Dienstag, 23. März 2010
  11. 11. Privacy Preferences  f is a privacy preference if it is a unary predicate on policies  privacy preference f is robust if, for all policies p ≤ p’, f(p’) f(p) • a robust preference that accepts a policy will also accept a more restrictive policy • is an upper bound on policy permissiveness Example: A preference to block web sites that use home telephone numbers for telemarketing is robust, whereas a preference to block web sites that do not use home telephone numbers for telemarketing is not robust. 11 Dienstag, 23. März 2010
  12. 12. A P3P Preference Exchange Language (APPEL) An APPEL preference consists of a set of rules, each giving a judgment (block, limited, or request) and a condition under which to issue that judgment: • rules are processed in order, halting at the first rule that matches • a rule matches if its pattern (expression) is satisfied by the policy • every APPEL expression has a connective attribute that defines the logical operators between its subexpressions: or, and, non-or, non-and, or-exact, and-exact Example: 1. Requests for personal information which will be given out to 3rd parties should be rejected. 2. The user does not mind revealing click-stream and user agent information to sites that collect no other information. However, she insists that the service provides some form of assurance. 3. All other requests for data transfer should result in a warning (indicating a conflict with her privacy preferences). 12 Dienstag, 23. März 2010
  13. 13. APPEL rule <appel:RULE behavior="limited" prompt="yes" description="Warning! Data may be shared."> <p3p:POLICY> description <p3p:STATEMENT> <p3p:RECIPIENT appel:connective="or" > <p3p:same/> connective pattern <p3p:other-recipient/> - or (R ∩ E ≠ ∅) <p3p:public/> <p3p:unrelated/> - and (R ⊆ E) </p3p:RECIPIENT> - non-or (R ∩ E = ∅) </p3p:STATEMENT> - non-and (R E ≠ ∅) </p3p:POLICY> - or-exact (E ⊆ R) </appel:RULE> Behavior - and-exact (E = R) - request - block - limited 13 Dienstag, 23. März 2010
  14. 14. Example Ruleset <appel:APPEL xmlns:APPEL="http://www.w3.org/2001/02/APPELv1"> <appel:RULESET crtdby="W3C" crtdon=“1999-11-03T09:21:32-05:00"> <appel:RULE behavior=“block" description="Service collects identifiable data for 3rd parties"> <POLICY><STATEMENT> <DATA-GROUP><CATEGORIES connective=“or"> <physical/><demographic/><userid/> </CATEGORIES></DATA-GROUP> <RECIPIENT connective=“or"> <same/><other-recipient/><public/><delivery/><unrelated/> <RECIPIENT/> </STATEMENT></POLICY> </appel:RULE> <appel:RULE behavior=“request" description="Service only collects clickstream data"> <POLICY><STATEMENT> <DATA-GROUP connective=“or-exact"> <DATA name="Dynamic.HTTP.UserAgent"/> <DATA name="Dynamic.ClickStream.Server"/> </DATA-GROUP> </STATEMENT> <DISCLOSURE discURI="*"/> <DISPUTES-GROUP><DISPUTES org="*"/></DISPUTES-GROUP> </POLICY> </appel:RULE> <appel:RULE behavior=“limited" description="Suspicious Policy. Beware!"> <appel:OTHERWISE/> </appel:RULE> </appel:RULESET></appel:APPEL> 14 Dienstag, 23. März 2010
  15. 15. An Example Preference <appel:RULESET> <appel:RULE behavior="block" <POLICY> <STATEMENT> <PURPOSE connective="or"> <contact/> <telemarketing/> </PURPOSE> </STATEMENT> </POLICY> </appel:RULE> <appel:RULE behavior="request"> <appel:OTHERWISE/> </appel:RULE> </appel:RULESET> Block sites whose policies indicate that the information collected can be used for contact or telemarketing. 15 Dienstag, 23. März 2010
  16. 16. A non-robust APPEL preference <appel:RULE behavior="block" <POLICY> <STATEMENT connective="or"> <PURPOSE connective="non-and"> <telemarketing/> </PURPOSE> </STATEMENT> </POLICY> </appel:RULE> The connective non-and causes the rule to fire for policies that do not disclose the telemarketing purpose. 16 Dienstag, 23. März 2010
  17. 17. XPref [Agrawal et al, 2003]  uses minimal subset of XPath  provides significant expressive power  there is an APPEL to XPath translation  can express non-robust preferences  using XPref robustly is non-intuitive 17 Dienstag, 23. März 2010
  18. 18. Approximations to a robust XPref preference <RULE behavior="block" (A) condition= "/POLICY/STATEMENT/DATA-GROUP/* [ name(.) = "DATA" and @ref = "user.home-info.postal" ]" /> <RULE behavior="block" (B) condition= "/POLICY/STATEMENT/DATA-GROUP/* [ name(.) = "DATA" and ( @ref = "user.home-info.postal" or @ref = "user.home-info" or @ref = "user.home" ) ] /> <RULE behavior="block" condition= "/POLICY/STATEMENT/DATA-GROUP/* [ name(.) = "DATA" and (C) ( starts-with(@ref = "user.home-info.postal") or @ref = "user.home-info" or @ref = "user.home" ) ]" /> Block services that use my home address: #user.home-info.postal ⊫ a 18 Dienstag, 23. März 2010
  19. 19. Implementing P3P Using Database Technology [Agrawal et al, 2003] client-centric server-centric ★ 15-30 times faster ★ consumer must trust server 19 Dienstag, 23. März 2010
  20. 20. EPA: Privacy-aware business process design 2. Personalized use Law, regulations, privacy agreements, Rules 4. Anonymized use preferences, consent request ... authorization, 1a. Collection obligation form = data + rules Rules delete Data release anonymize Subject notify utilize 1b. Control 3. Depersonalized use disclose give consent Subject update or Guardian Data User repersonalize access or Authority depersonalize withdraw consent 20 Dienstag, 23. März 2010
  21. 21. „The Privacy Pie“ P3P Collect Notice Consent Enforce Audit Privacy Compliance Policy 21 Dienstag, 23. März 2010
  22. 22. Inside Business: Enriched Privacy Policy  Vocabulary defines scope:  Data, users, and purposes as hierarchies  Operations, obligations as lists  Rules authorize access: A [user category] should be [allowed or denied] the ability to perform [action] on [data category] for [purpose] under [condition] yielding an obligation to [obligation]. Example: “A nurse is allowed to perform write on GeneralMedicalInformation for MedicalFollowUp under InCharge(patient) yielding an obligation to notify(physicianInCharge).” 22 Dienstag, 23. März 2010
  23. 23. Choices for Enforcing Privacy  Do nothing and pray  Coding privacy policy into applications  cost of coding and maintenance becomes prohibitive  time to change to a new policy is far too large.  each of the applications has to be modified for each policy change  difficult reporting and auditing  Centralized Enforcement Infrastructure  centralized consent and policy management  centralized auditing and reporting  distributed enforcement 23 Dienstag, 23. März 2010
  24. 24. Enforcement Architecture Subject Data user Application Authorization Policy Engine Policy Monitor Deployment (and cache) Descr. Privacy Obligation Access Consent Policies log file log file Data Privacy Auditor Request + Officer Context Subject 24 Dienstag, 23. März 2010
  25. 25. Sticky-policy paradigm across domains Subject App3 AppA App1 Data + Policy App2 Data Policy Data Policy Enterprise 2 Enterprise 1 • Define “privacy boundaries” within the infrastructure that encompasses all the places where personal info is stored. • It should always be possible to determine the applicable privacy policy that was in force when a particular piece of personal info was collected. 25 Dienstag, 23. März 2010
  26. 26. What can be done with a policy? Organizational Laws, etc. Templates Boundaries Refinement/s, Practices S-Policy R-Policy Composition/s Ontologies Privacy Policy … Projection Efficiency? Abstr. Req. Composition Deployment Consistency Check Consent Concr. Req. Delegation & Context Audit Revocation? Obligations Data Application Trust mgmt? Assurance? 26 Dienstag, 23. März 2010
  27. 27. Desirable properties of a privacy language  guaranteed consistency  guaranteed safety  admitting local reasoning  closed under combination ★ all four goals cannot be achieved simultaneously in any language with minimum level of expressiveness ★ sequential semantics forces consistency 27 Dienstag, 23. März 2010
  28. 28. IBM Enterprise Privacy Architecture (EPA)  Consulting method for analysis Privacy and design Principles  Language for describing enterprise privacy policies  Architecture for enforcing (or monitoring) policies on data Privacy Strategy systems  Tools for detecting privacy violations Privacy Architecture 1 2 3 Classify, User access Technical analyze & consent enforcement http://www.zurich.ibm.com/security/enterprise-privacy/ Reference: Government of Alberta 28 Dienstag, 23. März 2010
  29. 29. Summary  current preference languages work only on the syntax of privacy policies  consumers and service providers have distinct perspectives  privacy promises must separate these modalities to have a sound link between promises and enforcement  P3P policies enforce their compact policies  enforcement at the service provider (enterprise) requires fine-grained usage control policies and management 29 Dienstag, 23. März 2010
  30. 30. References  R. Agrawal, J. Kiernan, R. Srikant and Y. Xu: “An XPath-based Preference Language for P3P.” 12th WWW Conference, 2003  R. Agrawal, J. Kiernan, R. Srikant and Y. Xu: “Implementing P3P Using Database Technology.” 19th Int’l. Conf. on Data Engineering (ICDE), 2003  A. Barth and J.C. Mitchell: “Enterprise Privacy Promises and Enforcement.” WITS’05, 2005.  T. Yu, N. Li, and A.I. Antón: “A Formal Semantics for P3P”. ACM Workshop on Secure Web Services (SWS), 2004.  G. Karjoth and M. Schunter: “A Privacy Policy Model for Enterprises.” 15th IEEE Computer Security Foundations Workshop, 271-281, 2002. www.csl.sri.com/programs/security/csfw/csfw15/ slides/karjoth.pdf 30 Dienstag, 23. März 2010

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