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End-to-End Architecture for Sensitive Data Handling: A Comprehensive Perspective

juanita-ocampo avatar
juanita-ocampo
28 diapositives · 401 Views · Nov 16, 2014

This paper addresses the need for an end-to-end architecture to handle sensitive data effectively. Topics covered include performance, preservation, distribution (P2P), privacy spectrum, prevention, detection, containment, and challenges in secure database management systems. The authors propose solutions like privacy-preserving OLAP, distributed architecture, data preservation in P2P systems, trust and reputation management, and privacy-preserving indexing. The text explores the motivation behind secure outsourcing, client-side processing, encryption, and optimizing query costs. It delves into challenges like privacy constraints, query decomposition, and execution cost minimization. Additionally, examples and algorithms for detection, containment, and entity resolution are discussed, emphasizing the importance of maintaining privacy preferences. The paper also includes practical examples, privacy policies, and a framework for controlling and managing private information across various applications.

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  1. Towards an end-to-end architecture forhandling sensitive data Hector Garcia-Molina Rajeev Motwani and students 1

  2. DB Perspective • Performance • Preservation • Distribution (P2P) • Bad Guys: • eavesdrop • corrupt • Trust

  3. DB Perspective • Preservation goal + easy preservation easy - - + privacy

  4. Privacy Spectrum • Prevention • Detection • Containment

  5. Prevention: Our Work • Privacy-Preserving OLAP • Distributed Architecture for Secure DBMS (P) • Data Preservation in P2P Systems • P2P Trust and Reputation Management (P) • P2P Privacy Preserving Indexing (P)

  6. Distributed Architecturefor Secure DBMS • Motivation: Outsourcing • Secure Database Provider (SDP) Encrypt ServiceProvider Client

  7. Performance Problem Encrypt ServiceProvider Client Query Q Q’ Client-side Processor Answer “Relevant Data” Problem: Q’ “SELECT *”

  8. The Power of Two DSP1 Client DSP2

  9. Basic Idea { CC# } { CC#, expDate, name } { expDate, name }

  10. Another Example { salary + rand } { salary } { rand }

  11. The Power of Two DSP1 Q1 Query Q Client-side Processor Q2 DSP2 Key: Ensure Cost (Q1)+Cost (Q2)  Cost (Q)

  12. Challenges • Find a decomposition that • Obeys all privacy constraints • Minimizes execution cost for given workload • For given query, find good plan

  13. Most popular queries: • Select on a, b • Select on b, c R1(id, a, b) R2(id, b, c) Example R(id, a, b, c), privacy constraint: { a, b, c } R1(id, a, b) R2(id, b, c) … R1(id, a) R2(id, b, c) R1(id, a, b) R2(id, c) R1(id, a, c) R2(id, b, c)

  14. Detection: Our Work • Simulatable Auditing (P) • k-Anonymity • algorithms and hardness

  15. Containment: Our Work • Paranoid Platform for Privacy Preferences (P) • Entity Resolution

  16. Containment • Trusting • privacy policies • Paranoid

  17. Example: Trusting • Example P3P Policies: • Current purpose: completion and support of the recurring subscription activity • Recipients: DealsRUs and/or entities acting as their agents or entities for whom DealsRUs are acting as an agent... (1) browse policy (2) give info alice (3) cross fingers dealsRus

  18. Example: Email (1) temp a12@w (2) a12@w (3) To:a12@w (4) To: a@z alice’sagent alicea@z dealsRus

  19. API API Strategy/Reference Implementation P4P: Paranoid Platformfor Privacy Preferences Framework Data/Control Types: t1 ... tn

  20. Private Information sharable accountable no integration control no predicate input limited time use complete privacy function copy identifier service handle input to predicate ownership individual organization

  21. Entity Resolution • Applications: • mailing lists, customer files, counter-terrorism, ... e2 e1 N: a A: b CC#: c Ph: e N: a Exp: d Ph: e

  22. 1.0 1.0 0.7 Nm: Alice Ad: 32 Fox Ph: 5551212 Nm: Alice Ad: 32 Fox Ph: 5551212 Nm: Alice Ad: 32 Fox Ph: 5551212 Privacy Alice 1.0 1.0 Nm: Alice Ad: 32 Fox Nm: Alice Ad: 32 Fox Ph: 5551212 Ad: 14 Cat Bob

  23. 1.0 0.7 Nm: Alice Ad: 32 Fox Ph: 5551212 Nm: Alice Ad: 32 Fox Ph: 5550000 Leakage Alice Bob L = 0.6 (between 0 and 1)

  24. 1.0 Nm: Alice Ad: 32 Fox Ph: 5551212 Multi-Record Leakage Alice r1, L = 0.9 r2, L = 0.8 r3, L = 0.7 Bob LL = 0.9 (between 0 and 1, e.g., max L)

  25. Q1: Added Vulnerability? p Alice r1 r2 r3 r4 Bob r4 may cause Bob’s records to snap together! ΔLL = ??

  26. Q2: Disinformation? p Alice r1 r2 r3 r4 (lies) Bob What is most cost effective disinformation? ΔLL = ??

  27. Q3: Verification? p Alice hypothesis h (0.6) r1, 0.9 r2, 0.8 r3, 0.7 ... Bob What is best fact to verify to increase confidence in hypothesis?

  28. Privacy Spectrum • Prevention • Detection • Containment