Maximum Diversity in Web Application User Action Privacy Popular web application features, like autosuggestion and autocorrection, are wellknown to amply reveal input actions entered by their users to any eavesdroppers, by generating sufficiently unique network activity bursts on actions. Some techniques, like kanonymity and ldiversity, already exist in the literature for achieving different levels of privacy of user actions by making the generated network bursts for different actions indistinguishable from each other. We present a method that blends network bursts of not entire actions, but their probabilistic portions, with each other. Our method thus results in the maximum possible amount of ldiversity, namely kdiversity, and is a sianificant improvement over the existing ones.

Duet: An Expressive Higherorder Language and Linear Type System for Statically Enforcing Differential Privacy During the past decade, differential privacy has become the gold standard for protecting the privacy of individuals. However, verifying that a particular program provides differential privacy often remains a manual task to be completed by an expert in the field. Languagebased techniques have been proposed for fully automating proofs of differential privacy via type system design, however these results have lagged behind advances in differentiallyprivate algorithms, leaving a noticeable gap in programs which can be automatically verified while also providing stateoftheart bounds on privacy.  :84,153,propose Duet, an expressive higherorder language, linear type system and tool for automatically verifying differential privacy of generalpurpose higherorder programs. In addition to general purpose programming, Duet supports encoding machine learning algorithms such as stochastic gradient descent, as well as common auxiliary data analysis tasks such as clipping, normalization and hyperparameter tuning  each of which are particularly challenging to encode in a statically verified differential privacy framework.  present a core design of the Duet language and linear type system, and complete key proofs about privacy for welltyped programs. We then show how to extend Duet to support realistic machine learning applications and recent variants of differential privacy which result in improved accuracy for many practical differentially private algorithms. Finally, we implement several differentially private machine learning algorithms in Duet which have never before been automatically verified by a languagebased tool, and we present experimental results which demonstrate the benefits of Duetu0027s language design in terms of accuracy of trained machine learning models.