Inference Problem Privacy Preserving Data Mining

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Inference ProblemPrivacy Preserving Data Mining
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Readings and Assignments

• Required
• Pfleeger Chapter 6.5
• Interesting reading
• I. Moskowitz, M. H. Kang Covert Channels Here
  to Stay? http//citeseer.nj.nec.com/cache/papers/c
  s/1340/httpzSzzSzwww.itd.nrl.navy.milzSzITDzSz554
  0zSzpublicationszSzCHACSzSz1994zSz1994moskowitz-co
  mpass.pdf/moskowitz94covert.pdf
• Jajodia, Meadows Inference Problems in
  Multilevel Secure Database Management Systems
  http//www.acsac.org/secshelf/book001/book001.html
  , essay 24

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Indirect Information Flow Channels

• Covert channels
• Inference channels

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Communication Channels

• Overt Channel designed into a system and
  documented in the user's manual
• Covert Channel not documented. Covert channels
  may be deliberately inserted into a system, but
  most such channels are accidents of the system
  design.

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Covert Channel

• Timing Channel based on system times
• Storage channels not time related communication
• Can be turned into each other

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Inference Channels

• Non-sensitive
• information

Sensitive Information

Meta-data

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Inference Channels

• Statistical Database Inferences
• General Purpose Database Inferences

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Statistical Databases

• Goal provide aggregate information about groups
  of individuals
• E.g., average grade point of students
• Security risk specific information about a
  particular individual
• E.g., grade point of student John Smith
• Meta-data
• Working knowledge about the attributes
• Supplementary knowledge (not stored in database)

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Types of Statistics

• Macro-statistics collections of related
  statistics presented in 2-dimensional tables
• Micro-statistics Individual data records used
  for statistics after identifying information is
  removed

Sex\Year 1997 1998 Sum
Female 4 1 5
Male 6 13 19
Sum 10 14 24
Sex Course GPA Year
F CSCE 590 3.5 2000
M CSCE 590 3.0 2000
F CSCE 790 4.0 2001
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Statistical Compromise

• Exact compromise find exact value of an
  attribute of an individual (e.g., John Smiths
  GPA is 3.8)
• Partial compromise find an estimate of an
  attribute value corresponding to an individual
  (e.g., John Smiths GPA is between 3.5 and 4.0)

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Methods of Attacks and Protection

• Small/Large Query Set Attack
• C characteristic formula that identifies groups
  of individuals
• If C identifies a single individual I, e.g.,
  count(C) 1
• Find out existence of property
• If count(C and D)1 means I has property D
• If count(C and D)0 means I does not have D
• OR
• Find value of property
• Sum(C, D), gives value of D

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Small/Large Query Set Attack cont.

• Protection from small/large query set attack
  query-set-size control
• A query q(C) is permitted only if
• N-n ? C ? n , where n ? 0 is a parameter of
  the database and N is all the records in the
  database

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Tracker attack
q(C) is disallowed
CC1 and C2 TC1 and C2
Tracker
C
C2
C1
q(C)q(C1) q(T)
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Tracker attack
q(C and D) is disallowed
CC1 and C2 TC1 and C2
C
Tracker
C2
C1
C and D
q(C and D) q(T or C and D) q(T)
D
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Query overlap attack
Q(John)q(C1)-q(C2)
C1
C2
Kathy
Paul
John
Eve
Max
Fred
Mitch
Protection query-overlap control
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Insertion/Deletion Attack

• Observing changes overtime
• q1q(C)
• insert(i)
• q2q(C)
• q(i)q2-q1
• Protection insertion/deletion performed as pairs

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Statistical Inference Theory

• Give unlimited number of statistics and correct
  statistical answers, all statistical databases
  can be compromised (Ullman)

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Inferences in General-Purpose Databases

• Queries based on sensitive data
• Inference via database constraints
• Inferences via updates

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Queries based on sensitive data

• Sensitive information is used in selection
  condition but not returned to the user.
• Example Salary secret, Name public
• ?Name?Salary25,000
• Protection apply query of database views at
  different security levels

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Database Constraints

• Integrity constraints
• Database dependencies
• Key integrity

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Integrity Constraints

• CAB
• Apublic, Cpublic, and Bsecret
• B can be calculated from A and C, i.e., secret
  information can be calculated from public data

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Database Dependencies

• Metadata
• Functional dependencies
• Multi-valued dependencies
• Join dependencies
• etc.

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Functional Dependency

• FD A ? B, that is for any two tuples in the
  relation, if they have the same value for A, they
  must have the same value for B.
• Example FD Rank ? Salary
• Secret information Name and Salary together
• Query1 Name and Rank
• Query2 Rank and Salary
• Combine answers for query1 and 2 to reveal Name
  and Salary together

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Key integrity

• Every tuple in the relation have a unique key
• Users at different levels, see different versions
  of the database
• Users might attempt to update data that is not
  visible for them

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Example
Secret View
Name (key) Salary Address
Black P 38,000 P Columbia S
Red S 42,000 S Irmo S
Public View
Name (key) Salary Address
Black P 38,000 P Null P
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Updates
Public User
Name (key) Salary Address
Black P 38,000 P Null P

• Update Blacks address to Orlando
• Add new tuple (Red, 22,000, Manassas)
• If
• Refuse update covert channel
• Allow update
• Overwrite high data may be incorrect
• Create new tuple which data it correct
• (polyinstantiation) violate key constraints

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Updates
Secret user
Name (key) Salary Address
Black P 38,000 P Columbia S
Red S 42,000 S Irmo S

• Update Blacks salary to 45,000
• If
• Refuse update denial of service
• Allow update
• Overwrite low data covert channel
• Create new tuple which data it correct
• (polyinstantiation) violate key constraints

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Inference Problem

• No general technique is available to solve the
  problem
• Need assurance of protection
• Hard to incorporate outside knowledge

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The Inference Problem

• General Purpose Database
• Non-confidential data Metadata ?
• Undesired Inferences
• Web Enabled Data
• Non-confidential data Metadata (data and
  application semantics) Computational Power
  Connectivity ? Undesired Inferences

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Correlated Inference
Object. waterSource Object
basin waterSource place Object
district place address place
base Object fort base
Base
Place
base
Public
Public
Water source
Water Source
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Inference Control
Access Control
Confidential
Public
X
Misinfo
Organizational Data
Attacker
X
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Inference Control
Confidential
Public
Misinfo
Organizational Data

• ACCESS and INFERENCE CONTROL POLICY
• Logic-based inference detection
• Exact and partial disclosure
• Data and metadata protection
• Heterogeneous data manipulation
• Metadata discovery

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Data Mining and Privacy

• Statistical inference
• K-anonymity
• Correlation
• General inference
• Pattern ? metadata
• Biased learning

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Next Class

• Midterm exam review