---
_id: '3502'
abstract:
- lang: eng
text: 'In content-driven reputation systems for collaborative content, users gain
or lose reputation according to how their contributions fare: authors of long-lived
contributions gain reputation, while authors of reverted contributions lose reputation.
Existing content-driven systems are prone to Sybil attacks, in which multiple
identities, controlled by the same person, perform coordinated actions to increase
their reputation. We show that content-driven reputation systems can be made resistant
to such attacks by taking advantage of thefact that the reputation increments
and decrements depend on content modifications, which are visible to all. We present
an algorithm for content-driven reputation that prevents a set of identities from
increasing their maximum reputation without doing any useful work. Here, work
is considered useful if it causes content to evolve in a direction that is consistent
with the actions of high-reputation users. We argue that the content modifications
that require no effort, such as the insertion or deletion of arbitrary text, are
invariably non-useful. We prove a truthfullness result for the resulting system,
stating that users who wish to perform a contribution do not gain by employing
complex contribution schemes, compared to simply performing the contribution at
once. In particular, splitting the contribution in multiple portions, or employing
the coordinated actions of multiple identities, do not yield additional reputation.
Taken together, these results indicate that content-driven systems can be made
robust with respect to Sybil attacks. Copyright 2008 ACM.'
acknowledgement: 'This research has been partially supported by the CITRIS: Center
for Information Technology Research in the Interest of Society.'
author:
- first_name: Krishnendu
full_name: Krishnendu Chatterjee
id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87
last_name: Chatterjee
orcid: 0000-0002-4561-241X
- first_name: Luca
full_name: de Alfaro, Luca
last_name: De Alfaro
- first_name: Ian
full_name: Pye, Ian
last_name: Pye
citation:
ama: 'Chatterjee K, De Alfaro L, Pye I. Robust content-driven reputation. In: ACM;
2008:33-42. doi:10.1145/1456377.1456387
'
apa: 'Chatterjee, K., De Alfaro, L., & Pye, I. (2008). Robust content-driven
reputation (pp. 33–42). Presented at the AISec: Artificial Intelligence and Security,
ACM. https://doi.org/10.1145/1456377.1456387
'
chicago: Chatterjee, Krishnendu, Luca De Alfaro, and Ian Pye. “Robust Content-Driven
Reputation,” 33–42. ACM, 2008. https://doi.org/10.1145/1456377.1456387 .
ieee: 'K. Chatterjee, L. De Alfaro, and I. Pye, “Robust content-driven reputation,”
presented at the AISec: Artificial Intelligence and Security, 2008, pp. 33–42.'
ista: 'Chatterjee K, De Alfaro L, Pye I. 2008. Robust content-driven reputation.
AISec: Artificial Intelligence and Security, 33–42.'
mla: Chatterjee, Krishnendu, et al. Robust Content-Driven Reputation. ACM,
2008, pp. 33–42, doi:10.1145/1456377.1456387
.
short: K. Chatterjee, L. De Alfaro, I. Pye, in:, ACM, 2008, pp. 33–42.
conference:
name: 'AISec: Artificial Intelligence and Security'
date_created: 2018-12-11T12:03:40Z
date_published: 2008-10-31T00:00:00Z
date_updated: 2021-01-12T07:43:54Z
day: '31'
doi: '10.1145/1456377.1456387 '
extern: 1
month: '10'
page: 33 - 42
publication_status: published
publisher: ACM
publist_id: '2885'
quality_controlled: 0
status: public
title: Robust content-driven reputation
type: conference
year: '2008'
...