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Database Security and Privacy

Database Security and Privacy. CSCI283 Fall 2005 GWU. Announcements. Final Exam: December 6, 2006 Regular classroom: Gelman 607 Regular class hours: 7:10-9:40 pm Syllabus: Lectures 6, 8-13

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Database Security and Privacy

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  1. Database Security and Privacy CSCI283 Fall 2005 GWU

  2. Announcements Final Exam: December 6, 2006 Regular classroom: Gelman 607 Regular class hours: 7:10-9:40 pm Syllabus: Lectures 6, 8-13 Block and Stream Ciphers, Public Key Cryptography, Public Key Infrastructure, Identity, Authentication, Malicious Logic, Risk Analysis, Database Privacy, Information Flow and Covert Channels; slides and associated readings. CS283/Fall05/GWU/Vora/Databases

  3. Databases provide • Shared access • Controlled access • Data consistency • Data integrity • Minimal redundancy CS283/Fall05/GWU/Vora/Databases

  4. Database Security Requirements • Integrity • Physical • Logical • element • Access Control • User Authentication • Availability • Auditability CS283/Fall05/GWU/Vora/Databases

  5. Integrity • Physical integrity: i.e. response to power failures and other physical problems • Backup • Transaction log to update since previous backup • Integrity • Field checks • Access control and consistency among values • Maintaining change logs CS283/Fall05/GWU/Vora/Databases

  6. More Security Requirements • Auditability • Helps determine if inappropriate disclosure has occurred • Helps track divulged information to prevent inference (example, if it is known that I am a woman and over 40, it is known that I am losing Calcium) • Difficult to record access of fields; often a field is reported to have been accessed when it has not – e.g. SELECT all entries with ZIP 20007 CS283/Fall05/GWU/Vora/Databases

  7. More Security Requirements • Access Control • Inference is a problem (fields are related; knowledge of race can be a good predictor of salary, for example) • Size and granularity different from access control in OS • User Authentication • DBMS does its own authentication because no trusted path between DBMS and OS when DBMS is an application program on top of OS • Availability important because of shared access goal. CS283/Fall05/GWU/Vora/Databases

  8. Integrity and Consistency – Two-Phase Update Assign seats to passengers Phase I – intent phase Collect all resources required to complete task Shadow values store key data points Committing: Set commit-flag. Marks end of intent Phase II – commit phase Shadow values are copied CS283/Fall05/GWU/Vora/Databases

  9. Seat assignments intent phase if COMMIT-FLAG == SET loop or halt else seat.shadow_name = John Doe seat.shadow_flag = taken commit phase COMMIT-FLAG = SET seat.name = seat.shadow_name seat.flag = seat.shadow_flag Unset COMMIT-FLAG CS283/Fall05/GWU/Vora/Databases

  10. Consistency - Monitors Checks values for consistency with field type, or with rest of database • Range comparisons • State constraints (for example, if all activities completed, COMMIT-FLAG should not be SET. This is a state constraint which might not be satisfied if power is lost between finishing activities and unsetting COMMIT-FLAG) • Transition constraints can be difficult to implement CS283/Fall05/GWU/Vora/Databases

  11. Sensitive Data • Inherently sensitive • From a sensitive source (request for information to be kept confidential) • Declared sensitive by database administrator • Part of a sensitive record (information on George Bush) or attribute (salary) • Sensitive in context of previously-released information CS283/Fall05/GWU/Vora/Databases

  12. Access Control • Access decisions depend on: • Availability of data • Acceptability of access • User Authentication • Types of Disclosures • Exact • Bounds • Negative/Positive Result • Existence of data • Probable value CS283/Fall05/GWU/Vora/Databases

  13. Multilevel Databases • Differentiated Security • Security at the level of element • Two levels not sufficient to represent sensitivity of data (hence multilevel) • Security of aggregate or function/combination of fields might be different from that of individual values • Not used much CS283/Fall05/GWU/Vora/Databases

  14. Separation • Partitioning: Each confidentiality level in a separate database • Difficult to update • Difficult for users who can access multiple security levels • Encryption • Problems? • Block Chaining • Integrity Lock • Secure hash of “element + position + sensitivity” stored with element • Sensitivity Lock • Identifier + Sensitivity encrypted CS283/Fall05/GWU/Vora/Databases

  15. Inference Believe a new fact based on other information (Sweeney) Suppose: Count(Female AND Caucasian AND HIV) = 1 And we know there is exactly one Female Caucasian in the database, Mary Smith We know Mary Smith has HIV even though we did not ask that, and the database would not reveal that One way to limit inference is to not allow divulging a small value CS283/Fall05/GWU/Vora/Databases

  16. count(a AND b AND c) = count(a) – count(a AND NOT(b AND c)) Count(F AND C AND HIV) = Count(F) – Count(F AND NOT(C AND HIV)) = Count(F) – Count(F AND (NOT C OR NOT HIV) Other methods for inference b a c CS283/Fall05/GWU/Vora/Databases

  17. Example of inference m1 = median(S1) m2 = median(S2) m1 < m2 S2 S1 What can you say about S1 and S2? About S1 AND NOT(S2) CS283/Fall05/GWU/Vora/Databases

  18. SOURCE: "Housing Characteristics 1990", Residential Energy Consumption Survey, Energy Information Administration, DOE/EIA-0314(90), page 54. CS283/Fall05/GWU/Vora/Databases

  19. ``Statistical Policy Working Paper 22 - Report on Statistical Disclosure Limitation Methodology'', Chapter 2, Federal Committee on Statistical Methodology, May 1994. Social Security Administration (SSA) rules prohibit tabulations in which a detail cell is equal to a marginal total or which would allow users to determine an individual's age within a five year interval, earnings within a $1000 interval or benefits within a $50 interval. CS283/Fall05/GWU/Vora/Databases

  20. ``Statistical Policy Working Paper 22 - Report on Statistical Disclosure Limitation Methodology'', Chapter 2, Federal Committee on Statistical Methodology, May 1994. CS283/Fall05/GWU/Vora/Databases

  21. ``Statistical Policy Working Paper 22 - Report on Statistical Disclosure Limitation Methodology'', Chapter 2, Federal Committee on Statistical Methodology, May 1994. CS283/Fall05/GWU/Vora/Databases

  22. ``Statistical Policy Working Paper 22 - Report on Statistical Disclosure Limitation Methodology'', Chapter 2, Federal Committee on Statistical Methodology, May 1994. CS283/Fall05/GWU/Vora/Databases

  23. ``Statistical Policy Working Paper 22 - Report on Statistical Disclosure Limitation Methodology'', Chapter 2, Federal Committee on Statistical Methodology, May 1994. CS283/Fall05/GWU/Vora/Databases

  24. ``Statistical Policy Working Paper 22 - Report on Statistical Disclosure Limitation Methodology'', Chapter 2, Federal Committee on Statistical Methodology, May 1994. CS283/Fall05/GWU/Vora/Databases

  25. Swapping microdata records • Find similar records • Swap some fields • Perform averages • Have a requirement: swap so as to preserve averages in all directions CS283/Fall05/GWU/Vora/Databases

  26. Aggregation Putting together records (from different databases) that share some attribute values to create a composite record. CS283/Fall05/GWU/Vora/Databases

  27. k-anonymity A single record has the same quasi-identifier as k-1 others. Attacks: Unsorted matching (see Fig. 3 – Sweeney) Complementary release (see Fig. 4 and 5 - Sweeney) Temporal Attack CS283/Fall05/GWU/Vora/Databases

  28. L. Sweeney. k-anonymity: a model for protecting privacy. International Journal on Uncertainty, Fuzziness and Knowledge-based Systems, 10 (5), 2002; 557-570. Race Birth Gender ZIP Problem t1 Black 1965 m 0214* short breath t2 Black 1965 m 0214* chest pain t3 Black 1965 f 0213* hypertension t4 Black 1965 f 0213* hypertension t5 Black 1964 f 0213* obesity t6 Black 1964 f 0213* chest pain t7 White 1964 m 0213* chest pain t8 White 1964 m 0213* obesity t9 White 1964 m 0213* short breath t10 White 1967 m 0213* chest pain t11 White 1967 m 0213* chest pain Example of k-anonymity, where k=2 and QI={Race, Birth, Gender, ZIP} CS283/Fall05/GWU/Vora/Databases

  29. L. Sweeney. k-anonymity: a model for protecting privacy. International Journal on Uncertainty, Fuzziness and Knowledge-based Systems, 10 (5), 2002; 557-570. Race ZIP Race ZIP Race ZIP Asian 02138 Person 02138 Asian 02130 Asian 02139 Person 02139 Asian 02130 Asian 02141 Person 02141 Asian 02140 Asian 02142 Person 02142 Asian 02140 Black 02138 Person 02138 Black 02130 Black 02139 Person 02139 Black 02130 Black 02141 Person 02141 Black 02140 Black 02142 Person 02142 Black 02140 White 02138 Person 02138 White 02130 White 02139 Person 02139 White 02130 White 02141 Person 02141 White 02140 White 02142 Person 02142 White 02140 Examples of k-anonymity tables; unsorted matching attack CS283/Fall05/GWU/Vora/Databases

  30. L. Sweeney. k-anonymity: a model for protecting privacy. International Journal on Uncertainty, Fuzziness and Knowledge-based Systems, 10 (5), 2002; 557-570. Race BirthDate Gender ZIP Problem Race BirthDate Gender ZIP Problem b 65 M 02141 short of breath b 65 M 02141 short of breath b 65 M 02141 chest pain b 65 M 02141 chest pain - 65 F 0213* painful eye b 65 F 02138 painful eye - 65 F 0213* wheezing b 65 F 02138 wheezing b 64 F 02138 obesity b 64 F 02138 obesity b 64 F 02138 chest pain b 64 F 02138 chest pain w 64 M 0213* short of breath w 60-69 M 02138 short of breath - 65 F 0213* hypertension w 60-69 - 02139 hypertension w 64 M 0213* obesity w 60-69 - 02139 obesity w 64 M 0213* fever w 60-69 - 02139 fever w 67 M 02138 vomiting w 60-69 M 02138 vomiting w 67 M 02138 back pain w 60-69 M 02138 back pain Complementary Release Attack Two k-anonymity tables k=2 CS283/Fall05/GWU/Vora/Databases

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