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The τ-Synopses system was designed to provide a run-time environment for multiple synopses. We focus on its utilization for synopses management in a single server. In this case, a critical function of the synopses management module is that of synopses reconciliation: given some limited memory space resource, determine which synopses to build and how to allocate the space among those synopses. We have developed a novel approach of synopses calibration for an efficient computation of synopses error estimation. Consequently we can now perform the synopses reconciliation in a matter of minutes, rather than hours. [ABSTRACT FROM AUTHOR]

Data synopses are concise representations of data sets, that enable effective processing of approximate queries to the data sets. The τ-Synopses is a system designed to provide a run-time environment for remote execution of multiple synopses for both relational as well as XML databases. The system can serve as an effective research platform for experimental evaluation and comparison of different synopses, as well as a platform for studying the effective management of multiple synopses in a federated or centralized environment. [ABSTRACT FROM AUTHOR]

In this paper we propose a video scrambling technique which scans a picture stored in a frame buffer along a pseudo-random space filling curve. We describe several efficient methods for generating cryptographically strong curves, and show that they actually decrease the bandwidth required to transmit the picture. [ABSTRACT FROM AUTHOR]

In recent years wavelet based synopses were shown to be effective for approximate queries in database systems. The simplest wavelet synopses are constructed by computing the Haar transform over a vector consisting of either the raw-data or the prefix-sums of the data, and using a greedy-heuristic to select the wavelet coefficients that are kept in the synopsis. The greedy-heuristic is known to be optimal for point queries w.r.t. the mean-squared-error, but no similar efficient optimality result was known for range-sum queries, for which the effectiveness of such synopses was only shown experimentally. We construct an operator that defines a norm that is equivalent to the mean-squared error over all possible range-sum queries, where the norm is measured on the prefix-sums vector. We show that the Haar basis (and in fact any wavelet basis) is orthogonal w.r.t. the inner product defined by this novel operator. This allows us to use Parseval-based thresholding, and thus obtain the first linear time construction of a provably optimal wavelet synopsis for range-sum queries. We show that the new thresholding is very similar to the greedy-heuristic that is based on point queries. For the case of range-sum queries over the raw data, we define a similar operator, and show that Haar basis is not orthogonal w.r.t. the inner product defined by this operator. [ABSTRACT FROM AUTHOR]

In recent years wavelets were shown to be effective data synopses. We are concerned with the problem of finding efficiently wavelet synopses for massive data sets, in situations where information about query workload is available. We present linear time, I/O optimal algorithms for building optimal workload-based wavelet synopses for point queries. The synopses are based on a novel construction of weighted inner-products and use weighted wavelets that are adapted to those products. The synopses are optimal in the sense that the subset of retained coefficients is the best possible for the bases in use with respect to either the mean-squared absolute or relative errors. For the latter, this is the first optimal wavelet synopsis even for the regular, non-workload-based case. Experimental results demonstrate the advantage obtained by the new optimal wavelet synopses, as well as the robustness of the synopses to deviations in the actual query workload. [ABSTRACT FROM AUTHOR]

An increasing number of web-sites require users to establish an account before they can access the information stored on that site ("personalized web browsing"). Typically, the user is required to provide at least a unique username, a secret password and an e-mail address. Establishing accounts at multiple web-sites is a tedious task. A security-and privacy-aware user may have to invent a distinct username and a secure password, both unrelated to his/her identity, for each web-site. The user may also desire mechanisms for anonymous e-mail. Besides the information that the user supplies voluntarily to the web-site, additional information about the user may flow (involuntarily) from the user's site to the web-site, due to the nature of the HTTP protocol and the cookie mechanism. This paper describes the Janus Personalized Web Anonymizer, which makes personalized web browsing simple, secure and anonymous by providing convenient solutions to each of the above problems. Janus serves as an intermediary entity between a user and a web-site. Given a user and a web-site, Janus automatically generates an alias - typically a username, a password and an e-mail address - that can be used to establish an anonymous account at the web-site. Different aliases are generated for each user, web-site pair; however the same alias is presented whenever a particular user visits a particular web-site. Janus frees the user from the burden of inventing and memorizing distinct usernames and secure passwords for each web-site, and guarantees that an alias (including an e-mail address) does not reveal the true identity of the user. Janus also provides mechanisms to complete an anonymous e-mail exchange from a web-site to a user, and filters the information-flow of the HTTP protocol to preserve user privacy. Thus Janus provides simultaneous user identification and user privacy, as required for anonymous personalized web browsing. [ABSTRACT FROM AUTHOR]