We study techniques for identifying an anonymous author via linguistic stylometry, i.e., comparing the writing style against a corpus of texts of known authorship. We experimentally demonstrate the effectiveness of our techniques with as many as 100,000 candidate authors. Given the increasing availability of writing samples online, our result has serious implications for anonymity and free speech -- an anonymous blogger or whistleblower may be unmasked unless they take steps to obfuscate their writing style. While there is a huge body of literature on authorship recognition based on writing style, almost none of it has studied corpora of more than a few hundred authors. The problem becomes qualitatively different at a large scale, as we show, and techniques from prior work fail to scale, both in terms of accuracy and performance. We study a variety of classifiers, both "lazy" and "eager," and show how to handle the huge number of classes. We also develop novel techniques for confidence estimation of classifier outputs. Finally, we demonstrate stylometric authorship recognition on texts written in different contexts. In over 20% of cases, our classifiers can correctly identify an anonymous author given a corpus of texts from 100,000 authors; in about 35% of cases the correct author is one of the top 20 guesses. If we allow the classifier the option of not making a guess, via confidence estimation we are able to increase the precision of the top guess from 20% to over 80% with only a halving of recall.

A system for private stream searching, introduced by Ostrovsky and Skeith, allows a client to provide an untrusted server with an encrypted search query. The server uses the query on a stream of documents and returns the matching documents to the client while learning nothing about the nature of the query. We present a new scheme for conducting private keyword search on streaming data which requires O ( m ) server to client communication complexity to return the content of the matching documents, where m is an upper bound on the size of the documents. The required storage on the server conducting the search is also O ( m ). The previous best scheme for private stream searching was shown to have O ( m log m ) communication and storage complexity. Our solution employs a novel construction in which the user reconstructs the matching files by solving a system of linear equations. This allows the matching documents to be stored in a compact buffer rather than relying on redundancies to avoid collisions in the storage buffer as in previous work. This technique requires a small amount of metadata to be returned in addition to the documents; for this the original scheme of Ostrovsky and Skeith may be employed with O ( m log m ) communication and storage complexity. We also present an alternative method for returning the necessary metadata based on a unique encrypted Bloom filter construction. This method requires O ( m log( t / m )) communication and storage complexity, where t is the number of documents in the stream. In this article we describe our scheme, prove it secure, analyze its asymptotic performance, and describe a number of extensions. We also provide an experimental analysis of its scalability in practice. Specifically, we consider its performance in the demanding scenario of providing a privacy preserving version of the Google News Alerts service.

In many applications, hosts in a peer to peer network may wish to maintain their anonymity or the privacy of their queries. In some applications, an even stronger guarantee is desirable: hosts would like to prevent others from determining whether they participate in the network at all. Darknets, or friend-to-friend networks, are one approach to preventing the discovery of hosts within a peer to peer network [1]. In such a network, hosts only form Internet connections with and directly communicate with a small set of hosts whose operators are known and trusted a priori. That is, each user only connects to her friends, trusting that her friends will not reveal her identity or existence in the network.

In several distributed systems a user should only be able to access data if a user posses a certain set of credentials or attributes. Currently, the only method for enforcing such policies is to employ a trusted server to store the data and mediate access control. However, if any server storing the data is compromised, then the confidentiality of the data will be compromised. In this paper we present a system for realizing complex access control on encrypted data that we call ciphertext-policy attribute-based encryption. By using our techniques encrypted data can be kept confidential even if the storage server is untrusted; moreover, our methods are secure against collusion attacks. Previous attribute-based encryption systems used attributes to describe the encrypted data and built policies into user's keys; while in our system attributes are used to describe a user's credentials, and a party encrypting data determines a policy for who can decrypt. Thus, our methods are conceptually closer to traditional access control methods such as role-based access control (RBAC). In addition, we provide an implementation of our system and give performance measurements.

We design an encryption scheme called Multi-dimensional Range Query over Encrypted Data (MRQED), to address the privacy concerns related to the sharing of network audit logs and various other applications. Our scheme allows a network gateway to encrypt summaries of network flows before submitting them to an untrusted repository. When network intrusions are suspected, an authority can release a key to an auditor, allowing the auditor to decrypt flows whose attributes (e.g., source and destination addresses, port numbers, etc.) fall within specific ranges. However, the privacy of all irrelevant flows are still preserved. We formally define the security for MRQED and prove the security of our construction under the decision bilinear Diffie-Hellman and decision linear assumptions in certain bilinear groups. We study the practical performance of our construction in the context of network audit logs. Apart from network audit logs, our scheme also has interesting applications for financial audit logs, medical privacy, untrusted remote storage, etc. In particular, we show that MRQED implies a solution to its dual problem, which enables investors to trade stocks through a broker in a privacypreserving manner.

A system for private stream searching allows a client to retrieve documents matching some search criteria from a remote server while the server evaluating the request remains provably oblivious to the search criteria. In this extended abstract, we give a high level outline of a new scheme for this problem and an experimental analysis of its scalability. The new scheme is highly efficient in practice. We demonstrate the practical applicability of the scheme by considering its performance in the demanding scenario of providing a privacy preserving version of the Google News Alerts service