Censorship-resistant Publishing

Emiran Curtmola, Alin Deutsch, K. K. Ramakrishnan and Divesh Srivastava
CS2010-0956
March 20, 2010

As the web evolves, it is becoming easier to form communities based on shared interests, and to create, publish, and query data on a wide variety of topics. In order to fully deliver on the promise of free data exchange, any community-supporting infrastructure needs to enforce the key requirement to preserve privacy of the association of content providers with potential sensitive published information. This privacy preserving publishing requirement prevents censorship, harassment, or discrimination of users by third parties. We propose a novel privacy-preserving distributed infrastructure in which data resides only with the publishers owning it. The infrastructure disseminates user queries to publishers, who answer them at their own discretion. The infrastructure enforces a publisher k-anonymity guarantee, which prevents leakage of information about which publishers are capable of answering a certain query. Given the virtual nature of the global data collection, we study the challenging problem of efficiently locating publishers in the community that contain data items matching a specified query. We propose a distributed index structure, UQDT, that is organized as a union of Query Dissemination Trees (QDTs), and realized on an overlay (i.e., logical) network infrastructure. Each QDT has data publishers as its leaf nodes, and overlay network nodes as its internal nodes; each internal node routes queries to publishers, based on a summary of the data advertised by publishers in its subtrees. We experimentally evaluate design tradeoffs, and demonstrate that UQDT can maximize throughput by preventing any overlay network node from becoming a bottleneck.


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