Abstract. We continue a line of research initiated in [10, 11] on privacypreserving statistical databases. Consider a trusted server that holds a database of sensitive information. Given a query function f mapping databases to reals, the so-called true answer is the result of applying f

Abstract A promising technique for protecting privacy and integrity of sensitive data is to statically check information flow within programs that manipulate the data. While previous work has proposed programming language extensions to allow this static checking, the resulting languages are too

simultaneously track multiple sources of sensitive data. TaintDroid’s efficiency to perform real-time analysis stems from its novel system design that leverages the mobile platform’s virtualized system architecture. TaintDroid incurs only 14 % performance overhead on a CPU-bound micro-benchmark with little

Publishing data about individuals without revealing sensitive information about them is an important problem. In recent years, a new definition of privacy called k-anonymity has gained popularity. In a k-anonymized dataset, each record is indistinguishable from at least k − 1 other records

A technique for creating touch sensitive surfaces is proposed which allows multiple, simultaneous users to interact in an intuitive fashion. Touch location information is determined independently for each user, allowing each touch on the common surface to be associated with a particular user

The central proposal of this article is that verbal reports are data. Accounting for verbal reports, as for other kinds of data, requires explication of the mech-anisms by which the reports are generated, and the ways in which they are sensitive to experimental factors (instructions, tasks, etc

Ensuring secure information ow within programs in the context of multiple sensitivity levels has been widely studied. Especially noteworthy is Denning's work in secure ow analysis and the lattice model [6][7]. Until now, however, the soundness of Denning's analysis has not been

quickly to changes in the scene which enables very sensitive detection of moving targets. We also show how the model can use color information to suppress detec-tion of shadows. The implementation of the model runs in real-time for both gray level and color imagery. Evaluation shows that this approach

, their value had not been demonstrated on difficult benchmarks such as the PASCAL datasets. Our system relies on new methods for discriminative training with partially labeled data. We combine a margin-sensitive approach for data-mining hard negative examples with a formalism we call latent SVM. A latent SVM

As more sensitive data is shared and stored by third-party sites on the Internet, there will be a need to encrypt data stored at these sites. One drawback of encrypting data, is that it can be selectively shared only at a coarse-grained level (i.e., giving another party your private key). We