C. Dellarocas. Immunizing online reputation reporting systems against unfair ratings and discriminatory behavior. In 2nd ACM Conference on Electronic Commerce, 2000.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....Before downloading resources, peers can call others as witnesses of the reliability of the prospective source. Beside recording the peers behavior in direct interactions, these systems can keep track also of the peers reliability as witnesses, thus allowing for properly weighting their judgment [8, 9]. Voters credibility information can be easily taken into account in our XRep protocol by requiring voters to declare their identity and sign their votes as in [8] Other proposals on the same line distinguish reliability of peers depending on the speci c context of interaction [1, 22] While ....

C. Dellarocas. Immunizing online reputation reporting systems against unfair ratings and discriminatory behavior. In Proc. of the 2nd ACM Conference on Electronic Commerce, Minneapolis, MN, USA, October 2000.

....by other agents. However, these solutions we believe not to be realistic as they do not provide any incentive for the agents to report the reputation information. Besides, each agent has to implement a rather complicated mechanism for judging the information it has received from its peers. [9] and [10] describe methods for protecting the reputation mechanism from unfair ratings. The approach the author takes is centralised, and relies on a central authority to ensure the safety properties of the mechanism. The same author [11] studies theoretical properties of eBay like online ....

C. Dellarocas. Immunizing Online Reputation Reporting Systems Against Unfair Ratings and Discriminatory Behaviour. In Proceedings of the 2nd ACM conference on Electronic Commerce, Minneapolis, MN, 2000.

....some protection against malicious users who might try to poison the central database with bogus data, or overwhelm it with data representing the particular bugs they wish to see fixed. Recent work on protecting privacy and preventing abuse in collaborative filtering systems may also be applicable [9, 11]. 6. CONCLUSIONS We have described a sampling infrastructure for gathering information about software from the set of runs produced by its user community. To ensure that rare events are ac curately represented, we use a Bernoulli process to do the sampling, and we have described an e#cient ....

C. Dellarocas. Immunizing online reputation reporting systems against unfair ratings and discriminatory behavior. In Proceedings of the 2nd ACM Conference on Electronic Commerce (EC-00), pages 150--157. ACM, 2000.

....ratings are of good quality if they are consistent to the majority opinions of the rating. Adversaries who submit fake or misleading feedbacks can still gain a good reputation as a rater in their method simply by submitting a large number of feedbacks and becoming the majority opinion. Dellarocas [10] proposed mechanisms to combat two types of cheating behavior when submitting feedbacks. The basic idea is to detect and filter out exceptions in certain scenarios using cluster filtering techniques. The technique can be applied into feedback based reputation systems to filter out the suspicious ....

C. Dellarocas. Immunizing online reputation reporting systems against unfair ratings and discriminatory behavior. In 2nd ACM Conference on Electronic Commerce, 2000.

....attacker from infiltrating one of the participating computers and thus corrupting the trust 7 repository. With careful design, the effect of a single intrusion could be limited, but distributed intrusions would remain an issue. Other techniques for reducing slander are discussed by Dellarocas [7], while Szabo [23] outlines the general limitations of reputation systems. In this context, the attack could be a worm installed on each of the participating computers, intercepting and modifying messages to the credit bureau before they were signed. The effects of the intrusion could be ....

C. Dellarocas. Immunizing online reputation reporting systems against unfair ratings and discriminatory behavior. In Proceedings of the 2nd ACM Conference on Electronic Commerce, pp. 150--157, Minneapolis, MN, Oct. 2000.

....obtain an aggregated index, which doesn t need to be weighted as the values are added together and no average is computed. UNFAIR RATING AND DISCRIMINATORY BEHAVIOUR The predictive value of reputation reporting systems can be compromised in situations where conspiring users give unfair ratings [4]. For examples, buyers can intentionally under evaluate sellers to put indirect pressure on price, delay. In return sellers can discriminate on the quality of service they provide to different buyers. On electronic marketplaces, cluster filtering or controlled anonymity can help reduce ....

....under evaluate sellers to put indirect pressure on price, delay. In return sellers can discriminate on the quality of service they provide to different buyers. On electronic marketplaces, cluster filtering or controlled anonymity can help reduce bad mouthing and negative discrimination [4]. In addition, normally only the buyer evaluates. In our case, the buyer is the owner of the platform. Collusion is therefore unavoidable as there is only one assessor. Seller evaluation will be more representative as it encompasses multiple companies. The impact of collusion could be ....

Dellarocas C., "Immunizing online reputation reporting systems against unfair ratings and discriminatory behavior", Proceedings of the 2nd ACM Conference on Electronic Commerce, Minneapolis, MN, October 17-20, 2000, http://ccs.mit.edu/dell/ec00reputation.pdf

....agents may only rarely have a previous negotiation history with each other, but this problem can be resolved through the use of reputation mechanisms that pool reported negotiation experiences over all agents. We would then of course have to account for the possibility of reputation sabotage [10]. Adaptive strategies are a good complement to reputation mechanisms since they reduce the negative consequences of getting misleading reputation information. Another tack is for contractor agents to negotiate with several subcontractors and select the best contract. This will increase the ....

Dellarocas, C. Immunizing online reputation reporting systems against unfair ratings and discriminatory behavior. in Proceedings of the 2nd ACM Conference on Electronic Commerce. 2000. Minneapolis, MN.

....fraction G of unfair raters that make it into the nearest neighbor set N . Analyzing the performance of cluster filtering when ratings vary over time . Experimenting with alternative clustering algorithms for separating N u and N l Some of the results of this ongoing work have been reported in Dellarocas (2000). Some additional results will be reported at the conference. ....

Dellarocas, C. "Immunizing Online Reputation Reporting Systems Against Unfair Ratings and Discriminatory Behavior," in Proceedings of the Second ACM Conference on Electronic Commerce, Minneapolis, MN, October 17-20, 2000.

....The range of attack types and corresponding responses is growing (see [3] for example) and seems limited only by human creativity. Even the mechanisms used to help avoid malicious agent behavior, such as reputation servers, are themselves prone to such attacks as collusive reputation manipulation [4]. Electronic markets also give unprecedented scope to the deployment of relatively untried market mechanisms whose vulnerabilities have not been fully understood. Large scale remote bidding has been enabled by ubiquitous telecommunications, for example, and combinatoric auctions have only recently ....

Dellarocas, C. Immunizing online reputation reporting systems against unfair ratings and discriminatory behavior. in Proceedings of the 2nd ACM Conference on Electronic Commerce. 2000. Minneapolis, MN.

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C. Dellarocas. Immunizing online reputation reporting systems against unfair ratings and discriminatory behavior. In 2nd ACM Conference on Electronic Commerce, 2000.

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C. Dellarocas, "Immunizing Online Reputation Reporting Systems against Unfair Ratings and Discriminatory Behavior," Proc. Second ACM Conf. Electronic Commerce, 2000.

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C. Dellarocas. Immunizing online reputation reporting systems against unfair ratings and discriminatory behaviour. In ACM Conference on Electronic Commerce. ACM, 2003. ISBN 1-58113-679-X.

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DELLAROCAS, C. Immunizing online reputation reporting systems against unfair ratings and discriminatory behavior. In Proc. of EC (Oct. 2000).

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C. Dellarocas. Immunizing online reputation reporting systems against unfair ratings and discriminatory behavior. In 2nd ACM Conference on Electronic Commerce, 2000.

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C. Dellarocas, "Immunizing Online Reputation Reporting Systems against Unfair Ratings and Discriminatory Behavior," Proc. Second ACM Conf. Electronic Commerce, 2000.

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C. Dellarocas. Immunizing Online Reputation Reporting Systems Against Unfair Ratings and Discriminatory Behavior. In ACM Conference on Electronic Commerce, pages 150--157, 2000.

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C. Dellarocas. Immunizing Online Reputation Reporting Systems Against Unfair Ratings and Discriminatory Behavior. In ACM Conference on Electronic Commerce, pages 150--157, 2000.

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C. Dellarocas. Immunizing Online Reputation Reporting Systems Against Unfair Ratings and Discriminatory Behavior. In ACM Conference on Electronic Commerce, pages 150--157, 2000.

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C. Dellarocas. Immunizing online reputation reporting systems against unfair ratings and discriminatory behavior. In Proceedings of the ACM Conference on Electronic Commerce, pages 150--157, Minneapolis, Minnesota, USA, 2000.

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C. Dellarocas, "Immunizing Online Reputation Reporting Systems against Unfair Ratings and Discriminatory Behavior," Proc. Second ACM Conf. Electronic Commerce, 2000.

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C. Dellarocas. Immunizing online reputation reporting systems against unfair ratings and discriminatory behavior. In 2nd ACM Conference on Electronic Commerce, 2000.

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C. Dellarocas. Immunizing online reputation reporting systems against unfair ratings and discriminatory behavior. In ACM Conference on Electronic Commerce, pages 150--157, 2000.

No context found.

C. Dellarocas. Immunizing Online Reputation Reporting Systems Against Unfair Ratings and Discriminatory Behavior. In ACM Conference on Electronic Commerce, pages 150--157, 2000.

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C. Dellarocas. Immunizing online reputation reporting systems against unfair ratings and discriminatory behavior. In Proceedings of the 2nd ACM Conference on Electronic Commerce (EC-00), pages 150--157. ACM, 2000.

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Dellarocas, C.: Immunizing online reputation reporting systems against unfair ratings and discriminatory behavior. In: Proceedings of the 2nd ACM conference on Electronic commerce, ACM Press (2000) 150--157

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C. Dellarocas. Immunizing online reputation reporting systems against unfair ratings and discriminatory behaviour. In ACM Conference on Electronic Commerce. ACM, 2003. ISBN 1-58113-679-X.

No context found.

Dellarocas, C.: Immunizing online reputation reporting systems against unfair ratings and discriminatory behavior. In: Proceedings of the ACM Conference on Electronic Commerce, Minneapolis, MN, USA (2000) 150--157

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C. Dellarocas. Immunizing online reputation reporting systems against unfair ratings and discriminatory behavior. In Proc. 2nd ACM Conference on Electronic Commerce, pages 150--157, Minneapolis, 2000.

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