Browsing by Author "Mathew, K."

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    GCPiN: Group caching for privacy in named data networking
    (2018) Kamath, A.A.; Jamadagni, C.; Anilkumar, A.; Mathew, K.; Tahiliani, M.P.
    Router architecture in Named Data Networks (NDN) is intricate, and entails in-network caching of Data packets. Protecting the privacy of this cached content, while maintaining the performance benefits obtained due to caching, is a major concern. Although this problem has been addressed, most existing solutions dramatically compromise the performance gain that NDN provides in order to ensure data privacy. In this paper, we formulate a new approach to enhance the privacy of cached content at each NDN router, while ensuring minimal performance loss. This is achieved by segregating NDN routers into groups, and maintaining a Distributed Content Store across each group. The proposed approach has been named Group Caching for Privacy in NDN (GCPiN). We provide a preliminary mathematical analysis which shows that GCPiN is a promising approach with scope for further evaluation. � 2017 IEEE.
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    GCPiN: Group caching for privacy in named data networking
    (Institute of Electrical and Electronics Engineers Inc., 2018) Kamath, A.A.; Jamadagni, C.; Anilkumar, A.; Mathew, K.; Tahiliani, M.P.
    Router architecture in Named Data Networks (NDN) is intricate, and entails in-network caching of Data packets. Protecting the privacy of this cached content, while maintaining the performance benefits obtained due to caching, is a major concern. Although this problem has been addressed, most existing solutions dramatically compromise the performance gain that NDN provides in order to ensure data privacy. In this paper, we formulate a new approach to enhance the privacy of cached content at each NDN router, while ensuring minimal performance loss. This is achieved by segregating NDN routers into groups, and maintaining a Distributed Content Store across each group. The proposed approach has been named Group Caching for Privacy in NDN (GCPiN). We provide a preliminary mathematical analysis which shows that GCPiN is a promising approach with scope for further evaluation. © 2017 IEEE.