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Dec 29, 2015
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Audit Mechanisms for Provable Risk Management and Accountable Data
Governance
Jeremiah Blocki, Nicolas Christin, Anupam Datta, Arunesh Sinha
Carnegie Mellon University
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Motivation
• Goal: treatment• Rigid access control hinders treatment• Permissive access control ⇒ privacy violations
Breach
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Auditing Audit – instead of rigid access control
Have a permissive access control regime Inspect accesses later to find violations Punish violators Repetitive process
Audits - Why Cry Over Spilt Milk? deters (near) rational employees
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Audit Challenges How much and what to audit?
Within budgetary constraints
How much to punish? Without de-motivating employees
Human in the loop Realistic model of human behavior
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Contribution A formal repeated game model of the audit process
An asymmetric equilibrium concept for games
An audit mechanism that is an equilibrium
Demonstrate usefulness of the model and equilibrium Predicts commonly observed phenomenon Predicts interesting results that calls for empirical
analysis
“essentially, all models are wrong, but some are useful” - George Box
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Outline Game Model
Equilibrium concepts
Equilibrium of Audit game
Predictions
Budget allocation and Fairness
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Repeated Game Model
The interaction repeats for each audit cycle (rounds of repeated game)
Typical actions in one round Emp action: (a, v) = (30, 2) Org action: (α, P) = (0.33, $100)
InspectAccess , Violate
Punishment rate
One auditcycle (round)
1 Game Model
J. Blocki, N. Christin, A. Datta, A. Sinha, Regret Minimizing Audits: A Learning-Theoretic Basis for Privacy Protection, IEEE Computer Security
Foundations, 2011
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Abstractions Independence assumptions
K types of violations (and accesses) Each employee acts independently for each type One repeated game for each type and employee
Parameters of the model known through studies[P][V] Risk factors (cost of violations) Audit cost Employee benefit in violating ….
Infinite horizon audit interaction for fixed parameters [Game Theory, Fudenberg and Tirole]
1 Game Model
[P] Ponemon Institute Studies, [V}Verizon Data Breach Studies
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Violation detection Given v violations and α fraction inspection
Expected number of violations caught internally - v. f(α)
Violations caught externally Assume fixed probability p of external detection Expected number – p.v.(1 – f(α))
1 Game Model
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Reputation Loss
Audit Cost
High Punishment Rate Loss
Payoffs Organization’s payoff
Employee’s payoff
1 Game Model
∝ α.a
∝ P∝ p.v.(1 – f(α))
∝ v.f(α)
PersonalBenefit
PunishmentPB.v P.v.(p.(1 – f(α)) + f(α))
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Additional Considerations Employees likely to not act rationally
Computationally constrained, Wrong beliefs ϵ probability of arbitrary behavior Org’s expected payoff for fixed P, α and employee
action (a,v) (1 - ϵ).(expected payoff with (a,v)) + ϵ.(expected payoff
with (a,a))
1 Game Model
Worst Case
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Graphical View of Payoffs Different employee best response partitions
organization’s action space Best response: v = 0 in deterred, v = a in un-
deterred More generally with non-linear payoff, a best
response of k number of violations defines a partition
1 Game Model
Fraction of accesses inspected (α)
Punishment Rate (P)
Deterred
Un-DeterredPB
α
P
0 1
32 a
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Subgame Perfect Equilibrium Strategy σ: nodes → actions Pay(σ1,σ2) = δ-discounted
sum of round payoffs (σ1,σ2) is NE if no unilateral
profitable deviation
Node N defines a subgame GN with restricted strategy σ1N
(σ1,σ2) is SPE if (σ1N,σ2N) is NE for GN
2Equilibrium concepts
{}
aa’
ab’
ba’
bb’
ab’; aa’
Action of P1 = {a, b}Action of P2 = {a,’ b’}
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Asymmetric approximate equilibrium Any SPE has the single stage deviation property
Pay(σ1sd,σ2) ≤ Pay(σ1,σ2)
Pay(σ1,σ2sd) ≤ Pay(σ1,σ2)
ϵ-SPE allows ϵ deviation by either player (ϵ1, ϵ2)-SPE allows ϵ1, ϵ2 deviation by player
P1, player P2
Special relevant case for security: (ϵ1, 0)-SPE Attacker (player P2) has no incentive to deviate Deviations by attacker may be costly for defender
2Equilibrium concepts
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Proposed equilibrium Organization: maximize
utility subject to best response of employee (Stackelberg games)
Commitment by organization
Employee plays best response
3 Equilibrium
The equilibrium attained is an (ϵ1, 0) SPE
α
PDeterred
Un-Deterred PB
ϵ1 is the sum of a) difference from optimum due to uncertainty in PBb) ϵ . maximum loss in reputation
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Advantages of commitment Makes the decision easier for not so rational
employee Computing single round best response is easier
Predictable employee response – not based on beliefs (beliefs affected by many factors)
Addresses the problem of equilibrium selection
“Open design: The design should not be secret”[SS]
3 Equilibrium
[SS] The Protection of Information in Computer Systems, Saltzer, J. H. and Schroeder, M. D.
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Doctors punished less than nurses Punishing a doctor is more costly for hospitals
Less audit cost, better tools means more inspections
Organizations audit to protect against greater loss
Increasing difference in cost of externally and internally caught violation leads to more inspections Should be studied empirically Can be used as an effective policy tool
Data Breach Notiifcation law [SR] vs. External audits
Predictions
4 Predictions
[SR]Romanosky, S., Hoffman, D., Acquisti, A., Empirical analysis of data breach litigation, International Conference on Information
Systems. (2011)
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Budget Allocation Organization plays multiple games Organization is constrained by total budget
Let the games be 1….n. Let the budget be B. Budget bi yields equilibrium Eq(bi) in game i Eq(bi) results in payoff Pay(bi) in game i Solve max ∑i Pay(bi) subject to ∑i bi ≤ B
5 Fair Auditing
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Towards Accountable Data Governance Utility maximization may lead to unfair
allocation
Add fairness constraints Minimum level of inspection, punishment rate for
each type
5 Fair Auditing
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Conclusion
Future Work: Study the accountability problem in depth Study complexity/algorithmic aspects of
computing equilibrium
Audit near-rational employees to optimize organization’s utility in a
fair manner
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References Zhao, X., Johnson, M.E., Access governance: Flexibility with
escalation and audit, Hawaii International International Conference on Systems Science, 2010
Zhang, N., Yu, W., Fu, X., Das, S.K.,Towards effective defense against insider attacks: The establishment of defender’s reputation, IEEE International Conference on Parallel and Distributed Systems. (2008)
Cheng, P.C., Rohatgi, P., Keser, C., Karger, P.A., Wagner, G.M., Reninger, A.S., Fuzzy Multi-Level Security : An Experiment on Quantified Risk-Adaptive Access Control, Proceedings of the IEEE Symposium on Security and Privacy. (2007)
Feigenbaum, J., Jaggard, A.D., Wright, R.N., Towards a formal model of accountability, Proceedings of the 2011 workshop on New security paradigms workshop. (2011)