A distributable APSE

A distributed Ada program library is a key element in a distributed Ada Program Support Environment (APSE). To implement this successfully, the program library universe as defined by the Ada Reference Manual must be broken up into independently manageable pieces. This in turn requires the support of a distributed database system, as well as a mechanism for identifying compilation units, linkable subprograms, and Ada types in a decentralized way, to avoid falling victim to the bottlenecks of a global database and/or global unique-identifier manager. It was found that the ability to decentralize Ada program library activity is a major advantage in the management of large Ada programs. Currently, there are 18 resource-catalog revision sets, each in its own Host Interface (HIF) partition, plus 18 partitions for testing each of these, plus 11 partitions for the top-level compiler/linker/program library manager components. Compiling and other development work can proceed in parallel in each of these partitions, without suffering the performance bottlenecks of global locks or global unique-identifier generation

Private Decayed Sum Estimation under Continual Observation

In monitoring applications, recent data is more important than distant data. How does this affect privacy of data analysis? We study a general class of data analyses - computing predicate sums - with privacy. Formally, we study the problem of estimating predicate sums {\em privately}, for sliding windows (and other well-known decay models of data, i.e. exponential and polynomial decay). We extend the recently proposed continual privacy model of Dwork et al. We present algorithms for decayed sum which are \eps-differentially private, and are accurate. For window and exponential decay sums, our algorithms are accurate up to additive 1/\eps and polylog terms in the range of the computed function; for polynomial decay sums which are technically more challenging because partial solutions do not compose easily, our algorithms incur additional relative error. Further, we show lower bounds, tight within polylog factors and tight with respect to the dependence on the probability of error