Differential privacy is a powerful tool for providing privacypreserving noisy query answers over statistical databases. It guarantees that the distribution of noisy query answers...
Processing of top-k queries has been attracting considerable attention. Much of the work assumes distributed data, with each site holding a different set of attributes for the sam...
We present an efficient protocol for the privacy-preserving, distributed learning of decision-tree classifiers. Our protocol allows a user to construct a classifier on a database h...
Scientific workflow systems increasingly store provenance information about the module executions used to produce a data item, as well as the parameter settings and intermediate...
Susan B. Davidson, Sanjeev Khanna, Tova Milo, Debm...
In this work we provide efficient distributed protocols for generating shares of random noise, secure against malicious participants. The purpose of the noise generation is to crea...
Cynthia Dwork, Krishnaram Kenthapadi, Frank McSher...