More and more important data is accumulated inside social networks. Limiting the flow of private information across a social network is very important, and most social networks provide sophisticated privacy settings to control this flow. Creating such extensive access control knobs makes the search for content a hard problem since each user sees a unique subset of all the data. In this work, we take a first step at integrating access control based on a social network in a search system. We describe a set of solutions to the problem, including what indexes to construct and how to filter out inaccessible results. An experimental analysis illustrates the tradeoffs of the various strategies, and we point out a set of interesting future research directions in this area.

To carry out work assignments, small groups distributed within a larger enterprise or collaborative community often need to share documents among themselves while shielding those documents from others ’ eyes. In this situation, users need an indexing facility that can quickly locate relevant documents that they are allowed to access, without (1) leaking information about the remaining documents, (2) imposing a large management burden as users, groups, and documents evolve, or (3) requiring users to agree on a central completely trusted authority. In order to achieve this aim user access levels and access control have to be reflected in the index structures and/or retrieval algorithms as well as in ranking the search results. My Ph.D. work focuses on building up an indexing infrastructure which supports confidential indexing, sharing and retrieval of unstructured information which is spread over a number of distributed access-controlled collections. In order to allow for effective and efficient indexing and retrieval in these settings, it considers aspects of confidentiality preservation within an outsourced inverted index, a DHT index structure in P2P networks as well as confidential top-k information retrieval. 1.

With the rise of cloud computing, it is increasingly attractive for end-users (organizations and individuals) to outsource the management of their data to a small number of largescale service providers. In this paper, we consider a user who wants to outsource storage and search for a corpus of web documents (e.g., an intranet). At the same time, the corpus may contain confidential documents that the organization does not want to reveal to the service provider. While past work has considered the problems of secure keyword search and secure indexing, all of the proposed tools require significant modifications to existing search engines and infrastructure. In this paper, we propose a system called PrivatePond, which allows confidential outsourced web search using an unmodified search engine. The system is built around the central idea of a secure indexable representation, which is attached to each document in the corpus, and constructed with the goal of balancing confidentiality and searchability. In addition, a secure local proxy is used to provide transparency to the end-user. While the idea of a secure indexable representation is very general, we propose a preliminary instantiation of this idea, which provides practical confidentiality. In addition, an experimental evaluation indicates that this indexable representation can provide high-quality search and ranking, similar to what is available using the unmodified corpus. 1.

Privacy-preserving document exchange among collaboration groups in an enterprise as well as across enterprises requires techniques for sharing and search of access-controlled information through largely untrusted servers. In these settings search systems need to provide confidentiality guarantees for shared information while offering IR properties comparable to the ordinary search engines. Top-k is a standard IR technique which enables fast query execution on very large indexes and makes systems highly scalable. However, indexing access-controlled information for top-k retrieval is a challenging task due to the sensitivity of the term statistics used for ranking. In this paper we present Zerber +R – a ranking model which allows for privacy-preserving top-k retrieval from an outsourced inverted index. We propose a relevance score transformation function which makes relevance scores of different terms indistinguishable, such that even if stored on an untrusted server they do not reveal information about the indexed data. Experiments on two real-world data sets show that Zerber +R makes economical usage of bandwidth and offers retrieval properties comparable with an ordinary inverted index. 1.

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