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.

Towards GDPR Compliant Software Design: A Formal Framework for Analyzing System Models Software systems nowadays store and process large amounts of personal data of individuals, rendering privacy protection a major issue of concern during their development. The EU General Data Protection Regulation addresses this issue with several provisions for protecting the personal data of individuals and makes it compulsory for companies and individuals to comply with the regulation. However, few methodologies have been considered to date to support GDPR compliance during system development. In this paper, we propose a processcalculus framework for formal modeling of software systems during the design phase, and validation of properties relating to the GDPR notion of Consent, the Right to Erasure, the Right to Access, and the Right to Rectification. Moreover, the framework enables the treatment of the notion of purpose through privacy policy satisfaction. Validation is performed with static analysis using type checking. Our work is the first step towards a framework that will implement PrivacybyDesign and GDPR compliance throughout the development cycle of a software system.