Online Auditing How may Auditors Inadvertently Compromise Your Privacy

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Online Auditing - How may Auditors Inadvertently
Compromise Your Privacy

• Kobbi Nissim
• Microsoft

With Nina Mishra HP/Stanford Work in progress
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The Setting
Statisticaldatabase

• Dataset dd1,,dn
• Entries di Real, Integer, Boolean
• Query q (f ,i1,,ik)
• f Min, Max, Median, Sum, Average, Count
• Bad users will try to breach the privacy of
  individuals

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The Data Privacy Game an Information-Privacy
Tradeoff
f
?i
f
f

• Private functions
• Want to hide ?i(d)di
• Information functions
• Want to reveal query answers f(di1,,dik)
• Major question what may be computed over d (and
  given to users) without breaching privacy?
• Confidentiality control methods
• Perturbation methods give noisy answers
• Query restriction methods limit the queries
  users may post, usually imposing some structure
  (e.g. size/overlap restrictions)

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Auditing

• AW89 classify auditing as a query restriction
  method
• Auditing of an SDB involves keeping up-to-date
  logs of all queries made by each user (not the
  data involved) and constantly checking for
  possible compromise whenever a new query is
  issued
• Partial motivation May allow for more queries to
  be posed, if no privacy threat occurs
• Early work Hofmann 1977, Schlorer 1976, Chin,
  Ozsoyoglu 1981, 1986
• Recent interest Kleinberg, Papadimitriou,
  Raghavan 2000, Li, Wang, Wang, Jajodia 2002,
  Jonsson, Krokhin 2003

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Auditing
Heres the answer
OR
Query denied (as the answer would cause privacy
loss)
Heres a new query qi1
Query log q1,,qi
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Design choices in Prior Work (1)

• Privacy definition
• Privacy breached (only) when a database entry may
  be deduced fully, or within some ? accuracy
• These privacy guarantees do not generally
  suffice
• Should take into account Adversarys
  computational power, prior knowledge, access to
  other databases
• Exact answers given
• Auditors viewed as a way to give quality
  answers???

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Design choices in Prior Work (2)

• 3. Which information is taken into account in the
  auditor decision procedure
• Decision made based on queries q1,,qi, qi1 and
  their answers a1,,ai, ai1
• Denials ignored
• 4. Offline vs. Online
• Offline auditing queries and answers checked for
  compromise at the end of the day
• Only detect breaches
• Online auditing answer/deny queries on the fly
• Prevent breaches just before they happen

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Example 1 Sum/Max auditing
di real, sum/max queries, privacy breached if
some di learned
q1 sum(d1,d2,d3)
sum(d1,d2,d3) 15
q2 max(d1,d2,d3)
Denied (the answer would cause privacy loss)
Oh well
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Some Prior Work on Auditors
Approx version in PTIME
Can we use the offline version for online
auditing?
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After Two Minutes
di real, sum/max queries, privacy breached if
some di learned
q1 sum(d1,d2,d3)
sum(d1,d2,d3) 15
q2 max(d1,d2,d3)
Denied (the answer would cause privacy loss)
There must be a reason for the denial
q2 is denied iff d1d2d3 5 I win!
Oh well
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Example 2 Interval Based Auditing
di ? 0,100, sum queries, ? 1 (PTIME)
q1 sum(d1,d2)
Sorry, denied
q2 sum(d2,d3)
sum(d2,d3) 50
d1,d2 ? 0,1 d3 ? 49,50
Denial ? d1,d2?0,1 or 99,100
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Sounds Familiar?
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Max Auditing
di real
q1 max(d1,d2,d3,d4)
M1234
M123 / denied
If denied d4M1234
M12 / denied
If denied d3M123
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Adversarys Success
q1 max(d1,d2,d3,d4)
If denied d4M1234
q2 max(d1,d2,d3)
Denied with probability 1/4
q2 max(d1,d2)
If denied d3M123
Denied with probability 1/3
Success probability 1/4 (1- 1/4)1/3 1/2
Recover 1/8 of the database!
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Boolean Auditing?
di Boolean
1 / denied
1 / denied

qi denied iff di di1 ? learn
database/complement
Let di,dj,dk not all equal, where qi-1, qi,
qj-1, qj, qk-1, qk all denied
1 / 2
Recover the entire database!
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Two Problems

• Obvious problem denied queries ignored
• Algorithmic problem not clear how to incorporate
  denials in the decision
• Subtle problem
• Query denials leak (potentially sensitive)
  information
• Users cannot decide denials by themselves

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A Spectrum of Auditors
Size overlap restriction Algebraic structure
lt utility
gt privacy
Note can work in unsafe region, but need to
prove denials do not leak crucial information
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Simulatable Auditing

• An auditor is simulatable if a simulator exists
  s.t.

?
Simulation ? denials do not leak information
self auditors in DN03
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Why Simulatable Auditors do not Leak Information?
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Summary

• Improper usage of auditors may lead to privacy
  breaches, due to information leakage in the
  decision procedure.
• Cell suppression / some k-anonymity methods
  should be checked similarly
• Should make sure offline auditors do not leak
  information in decision
• Simulatable auditors provably dont leak
  information
• Give best utility while still safe
• A launching point for further research on
  auditors
• Further research
• Auditors with more reasonable privacy guarantees