Workshop On Privacy In The Electronic Society, 2002
Abstract The conflict between Web service personalization and privacy is a challenge in the infor... more Abstract The conflict between Web service personalization and privacy is a challenge in the information society. In this paper we address this challenge by introducing MASKS, an architecture that provides data on the users' interests to Web services, without violating their privacy. The proposed approach hides the actual identity of users by classifying them into groups, according to their interests exhibited during the interaction with a Web service. By making requests on behalf of a group, instead of an individual user, MASKS provides ...
Proceeding of the ACM workshop on Privacy in the Electronic Society - WPES '02, 2002
Abstract The conflict between Web service personalization and privacy is a challenge in the infor... more Abstract The conflict between Web service personalization and privacy is a challenge in the information society. In this paper we address this challenge by introducing MASKS, an architecture that provides data on the users' interests to Web services, without violating their privacy. The proposed approach hides the actual identity of users by classifying them into groups, according to their interests exhibited during the interaction with a Web service. By making requests on behalf of a group, instead of an individual user, MASKS provides ...
Principles of Data Mining and Knowledge Discovery, 2002
Much of the existing work in machine learning and data mining has relied on devising efficient te... more Much of the existing work in machine learning and data mining has relied on devising efficient techniques to build accurate models from the data. Research on how the accuracyof a model changes as a function of dynamic updates to the databases is very limited. In this work we show that extracting this information: knowing which aspects of the model are changing; and how theyare changing as a function of data updates; can be verye effective for interactive data mining purposes (where response time is often more important than model qualityas long as model qualityi s not too far off the best (exact) model. In this paper we consider the problem of generating approximate models within the context of association mining, a keyda ta mining task. We propose a new approach to incrementallyg enerate approximate models of associations in evolving databases. Our approach is able to detect how patterns evolve over time (an interesting result in its own right), and uses this information in generating approximate models with high accuracy at a fraction of the cost (of generating the exact model). Extensive experimental evaluation on real databases demonstrates the effectiveness and advantages of the proposed approach.
Across a wide variety of fields, data are being collected and accumulated at a dramatic pace, and... more Across a wide variety of fields, data are being collected and accumulated at a dramatic pace, and therefore a new generation of techniques has been proposed to assist humans in extracting usefull information from the rapidly growing volumes of data. One of these techniques is the association rule discovery, a key data mining task which has attracted tremendous interest among data mining researchers. Due to its vast applicability, many algorithms have been developed to perform the association rule mining task. However, an immediate problem facing researchers is which of these algorithms is likely to make a good match with the database to be used in the mining operation. In this paper we consider this problem, dealing with both algorithmic and data aspects of association rule mining by performing a systematic experimental evaluation of different algorithms on different databases. We observed that each algorithm has different strengths and weaknesses depending on data characteristics. This careful analysis enables us to develop an algorithm which achieves better performance than previously proposed algorithms, specially on databases obtained from actual applications.
A routing overlay network is an application-layer overlay on the existing Internet routing substr... more A routing overlay network is an application-layer overlay on the existing Internet routing substrate that allows an alternative routing service. Recent studies have suggested that such networks might contain selfish nodes, which develop their strategies by considering only their own objectives. Extremely selfish nodes, called free-riders, might even refuse to share their resources with the network, thus making overlay service unavailable to the nodes that depend on them. The authors use a game-theoretic approach to evaluate the selfish-node mechanism and increase quality of service (QoS) by detecting and excluding free-riders
Workshop On Privacy In The Electronic Society, 2002
Abstract The conflict between Web service personalization and privacy is a challenge in the infor... more Abstract The conflict between Web service personalization and privacy is a challenge in the information society. In this paper we address this challenge by introducing MASKS, an architecture that provides data on the users' interests to Web services, without violating their privacy. The proposed approach hides the actual identity of users by classifying them into groups, according to their interests exhibited during the interaction with a Web service. By making requests on behalf of a group, instead of an individual user, MASKS provides ...
Proceeding of the ACM workshop on Privacy in the Electronic Society - WPES '02, 2002
Abstract The conflict between Web service personalization and privacy is a challenge in the infor... more Abstract The conflict between Web service personalization and privacy is a challenge in the information society. In this paper we address this challenge by introducing MASKS, an architecture that provides data on the users' interests to Web services, without violating their privacy. The proposed approach hides the actual identity of users by classifying them into groups, according to their interests exhibited during the interaction with a Web service. By making requests on behalf of a group, instead of an individual user, MASKS provides ...
Principles of Data Mining and Knowledge Discovery, 2002
Much of the existing work in machine learning and data mining has relied on devising efficient te... more Much of the existing work in machine learning and data mining has relied on devising efficient techniques to build accurate models from the data. Research on how the accuracyof a model changes as a function of dynamic updates to the databases is very limited. In this work we show that extracting this information: knowing which aspects of the model are changing; and how theyare changing as a function of data updates; can be verye effective for interactive data mining purposes (where response time is often more important than model qualityas long as model qualityi s not too far off the best (exact) model. In this paper we consider the problem of generating approximate models within the context of association mining, a keyda ta mining task. We propose a new approach to incrementallyg enerate approximate models of associations in evolving databases. Our approach is able to detect how patterns evolve over time (an interesting result in its own right), and uses this information in generating approximate models with high accuracy at a fraction of the cost (of generating the exact model). Extensive experimental evaluation on real databases demonstrates the effectiveness and advantages of the proposed approach.
Across a wide variety of fields, data are being collected and accumulated at a dramatic pace, and... more Across a wide variety of fields, data are being collected and accumulated at a dramatic pace, and therefore a new generation of techniques has been proposed to assist humans in extracting usefull information from the rapidly growing volumes of data. One of these techniques is the association rule discovery, a key data mining task which has attracted tremendous interest among data mining researchers. Due to its vast applicability, many algorithms have been developed to perform the association rule mining task. However, an immediate problem facing researchers is which of these algorithms is likely to make a good match with the database to be used in the mining operation. In this paper we consider this problem, dealing with both algorithmic and data aspects of association rule mining by performing a systematic experimental evaluation of different algorithms on different databases. We observed that each algorithm has different strengths and weaknesses depending on data characteristics. This careful analysis enables us to develop an algorithm which achieves better performance than previously proposed algorithms, specially on databases obtained from actual applications.
A routing overlay network is an application-layer overlay on the existing Internet routing substr... more A routing overlay network is an application-layer overlay on the existing Internet routing substrate that allows an alternative routing service. Recent studies have suggested that such networks might contain selfish nodes, which develop their strategies by considering only their own objectives. Extremely selfish nodes, called free-riders, might even refuse to share their resources with the network, thus making overlay service unavailable to the nodes that depend on them. The authors use a game-theoretic approach to evaluate the selfish-node mechanism and increase quality of service (QoS) by detecting and excluding free-riders
Uploads
Papers by Bruno Rocha