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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

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

distributed systems and models

wireless

losses

fiber

upon receive(x)

y= x + y

send y

cable

computer

losses

Distributed systems and Models

Van Neuman

crashes, bugs

fault models and approaches

bad

pros

cons

Crashes, losses

Dishonest users

hardware

rational

people

Fault models and Approaches

Byzantine faults

very bad

approaches example
Approaches: example

Preventing

speed governing

Masking

more roads

Dissuading

speed traps & fines

outline

Human nature

    • Collaborative dissemination
  • Social nature
    • Computation in Social Networks
Outline
collaborative dissemination attacks and dissuasion

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

collaborative dissemination challenges and solution

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

computation in social networks

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
computation in social networks1

A new model of entities

    • Reputation
    • Privacy
  • Computation
    • Set of entities
    • Input values
    • Compute

?

Computation in Social Networks
the s 3 problem definition candidate

S3 candidatequadruple where is an arbitrary set, is a metric space and is a symmetric function

The S3 problemDefinition: Candidate
the s 3 problem definition privacy

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
the s 3 problem definition privacy1

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
the s 3 problem definition faults

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
solving weak s 3 demo polling

-1

-1

-1

+2

+4

+4

+2

+4

+1

+1

+1

+1

+1

+4

-1

Solving (√,√,weak)-S3Demo: Polling
solving weak s 3

Theorem:

    • The protocol S3 computes aggregation functions for
Solving (√,√,weak)-S3
slide28

Impact of one faulty-node:

    • Voting:
    • Counting:
    • Aggregation along the ring: none
  • Relative error is

Solving (√,√,weak)-S3Proof: Accuracy

slide29

Exists (w.h.p.) two equivalent traces where inputs are swapped

Solving (√,√,weak)-S3Proof: Privacy

Output unchanged

p

q

Group with no faulty node

generalization

Can compute the multi-set of inputs

  • Can compute any regular function with a fixed input set
Generalization
conclusion perspectives

User-centric models practical solutions

  • Boundaries
  • Massively multi-player online games
Conclusion & perspectives