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Dissuasive Methods Against Cheaters in Distributed Systems. Kévin Huguenin Ph.D. defense, December 10 th 2010. TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: A A A A A A. wireless. losses. fiber. upon receive(x) y= x + y send y. cable. computer.
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Dissuasive Methods Against Cheaters in Distributed Systems Kévin Huguenin Ph.D. defense, December 10th 2010 TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: AAAAAA
wireless losses fiber upon receive(x) y= x + y send y cable computer losses Distributed systems and Models Van Neuman crashes, bugs
bad pros cons Crashes, losses Dishonest users hardware rational people Fault models and Approaches Byzantine faults very bad
Approaches: example Preventing speed governing Masking more roads Dissuading speed traps & fines
Detection • Punishment Dissuasive Approach: How To
Human nature • Collaborative dissemination • Social nature • Computation in Social Networks Outline
Epidemics Collaborative DisseminationPrinciple
Receive 12, 22, 31 4 guests Propose 12, 22, 31 (free SMS) period 4x Request 12, 22 (free SMS) 4x Send 12, 22 (MMS) Collaborative DisseminationAttacks and Dissuasion 3x 3x Propose less Less guests Send less Bias selection
Verifications • Decision Did he? I contacted C A Did B send what I asked? Yes he did log B C No Ok C A F B E G H D Z Y Z Z Z Y Collaborative DisseminationChallenges and Solution score 0 punished Propose less Less guests Send less Bias selection
E.g., polling • “Should partners be invited?” No way! I have to prevent this from happening But what if people find out? No! What if my partner had to learn? Yes! But it sounds cheesy… Computation in Social Networks
A new model of entities • Reputation • Privacy • Computation • Set of entities • Input values • Compute ? Computation in Social Networks
Scalable and Secure distributed computations in Social networks The S3 problemDefinition
S3 candidatequadruple where is an arbitrary set, is a metric space and is a symmetric function The S3 problemDefinition: Candidate
-Scalabilitymessage, spatial and computational complexities are The S3 problemDefinition: Scalability
-Accuracywhere The S3 problemDefinition: Accuracy
Probabilistic anonymity For any trace generated from a non-trivial configuration For any coalition of faulty nodesFor any non-faulty node Exists another trace (generated from ) s.t. The S3 problemDefinition: Privacy
Privacy: probabilistic anonymity • Discard trivial input configurations • (strong): trivial = inputs can be inferred from output alone • (weak): trivial = all inputs are equal The S3problemDefinition: Privacy
Model of faulty-nodes: • Deviate from the protocol BUT • Never behave in such a way that their misbehavior is detected with certainty The S3 problemDefinition: faults
groups of size (ring) Solving (√,√,weak)-S3Architecture
-1 -1 -1 +2 +4 +4 +2 +4 +1 +1 +1 +1 +1 +4 -1 Solving (√,√,weak)-S3Demo: Polling
Theorem: • The protocol S3 computes aggregation functions for Solving (√,√,weak)-S3
Messages • Memory Solving (√,√,weak)-S3Proof: Scalability
Solving (√,√,weak)-S3Proof: Accuracy • Attack: Voting +1 +1 +1 +1 +1
Attack: Counting +1 -1 +1 -1 -1 +5 +1 +1 Solving (√,√,weak)-S3Proof: Accuracy
Solving (√,√,weak)-S3Proof: Accuracy • Attack: Token corruption
Impact of one faulty-node: • Voting: • Counting: • Aggregation along the ring: none • Relative error is Solving (√,√,weak)-S3Proof: Accuracy
Exists (w.h.p.) two equivalent traces where inputs are swapped Solving (√,√,weak)-S3Proof: Privacy Output unchanged p q Group with no faulty node
Can compute the multi-set of inputs • Can compute any regular function with a fixed input set Generalization
User-centric models practical solutions • Boundaries • Massively multi-player online games Conclusion & perspectives