際際滷shows by User: rupasrimupparthi / http://www.slideshare.net/images/logo.gif 際際滷shows by User: rupasrimupparthi / Thu, 24 Oct 2013 08:14:29 GMT 際際滷Share feed for 際際滷shows by User: rupasrimupparthi Incentive Compatible Privacy Preserving Data Analysis /slideshow/incentive-compatible-privacy-preserving-data-analysis-27529287/27529287 ppt-131024081430-phpapp02
Now a days, data management applications have evolved from pure storage and retrieval of information to finding interesting patterns and associations from large amounts of data. With the advancement of Internet and networking technologies, more and more computing applications, including data mining programs, are required to be conducted among multiple data sources that scattered around different spots, and to jointly conduct the computation to reach a common result. However, due to legal constraints and competition edges, privacy issues arise in the area of distributed data mining, thus leading to the interests from research community of both data mining. In this project each party participates in a protocol to learn the output of some function f over the joint inputs of the parties. We mainly focus on the DNCC model instead of considering a probabilistic extension. Deterministic Non Cooperative Computation needs to be extended to include the possibility of collusion.]]>
Now a days, data management applications have evolved from pure storage and retrieval of information to finding interesting patterns and associations from large amounts of data. With the advancement of Internet and networking technologies, more and more computing applications, including data mining programs, are required to be conducted among multiple data sources that scattered around different spots, and to jointly conduct the computation to reach a common result. However, due to legal constraints and competition edges, privacy issues arise in the area of distributed data mining, thus leading to the interests from research community of both data mining. In this project each party participates in a protocol to learn the output of some function f over the joint inputs of the parties. We mainly focus on the DNCC model instead of considering a probabilistic extension. Deterministic Non Cooperative Computation needs to be extended to include the possibility of collusion.]]> Thu, 24 Oct 2013 08:14:29 GMT /slideshow/incentive-compatible-privacy-preserving-data-analysis-27529287/27529287 rupasrimupparthi@slideshare.net(rupasrimupparthi) Incentive Compatible Privacy Preserving Data Analysis rupasrimupparthi Now a days, data management applications have evolved from pure storage and retrieval of information to finding interesting patterns and associations from large amounts of data. With the advancement of Internet and networking technologies, more and more computing applications, including data mining programs, are required to be conducted among multiple data sources that scattered around different spots, and to jointly conduct the computation to reach a common result. However, due to legal constraints and competition edges, privacy issues arise in the area of distributed data mining, thus leading to the interests from research community of both data mining. In this project each party participates in a protocol to learn the output of some function f over the joint inputs of the parties. We mainly focus on the DNCC model instead of considering a probabilistic extension. Deterministic Non Cooperative Computation needs to be extended to include the possibility of collusion. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ppt-131024081430-phpapp02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Now a days, data management applications have evolved from pure storage and retrieval of information to finding interesting patterns and associations from large amounts of data. With the advancement of Internet and networking technologies, more and more computing applications, including data mining programs, are required to be conducted among multiple data sources that scattered around different spots, and to jointly conduct the computation to reach a common result. However, due to legal constraints and competition edges, privacy issues arise in the area of distributed data mining, thus leading to the interests from research community of both data mining. In this project each party participates in a protocol to learn the output of some function f over the joint inputs of the parties. We mainly focus on the DNCC model instead of considering a probabilistic extension. Deterministic Non Cooperative Computation needs to be extended to include the possibility of collusion.
Incentive Compatible Privacy Preserving Data Analysis from rupasri mupparthi
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