Automated Programming Frameworks For Analyzing Differential Privacy
Download Automated Programming Frameworks For Analyzing Differential Privacy PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Automated Programming Frameworks For Analyzing Differential Privacy book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Automated Programming Frameworks for Analyzing Differential Privacy
The accelerating growth of data has led to fruitful researches and real-world applications. While large datasets have benefited performance in many fields, recent incidents of data leakages and abuses have raised public concerns for data privacy. It has become a vital yet challenging task to balance individuals' privacy and data utilization for both researchers and data analysts. Among many attempts to tackle this challenge, differential privacy has become a de facto standard that provides a promising way to release individuals' sensitive data in a privacy-preserving manner. However, designing differentially-private algorithms is notoriously difficult and error-prone. Significant errors have happened even in peer-reviewed papers and systems. Such mistakes have led to researches on automated analysis of differential privacy algorithms to aid developers in the system design process. However, the limitations of existing tools either make the analysis time-consuming or fail to analyze sophisticated systems designed for differential privacy. In this dissertation, we propose a set of novel programming frameworks that target at three major aspects of automated analysis of differential privacy: verification, counterexample detection and program synthesis. For verification, we develop ShadowDP that embeds a novel proving technique named Shadow Execution to enable verification of a complex algorithm Report Noisy Max with very few annotations. Unlike prior works, ShadowDP is built upon standard program logics, making it easy to offload the verification of differential privacy to off-the-shelf verifiers. Our evaluations show ShadowDP is more efficient by orders of magnitude, compared with existing verifiers for differential privacy. For counterexample detection when a system fails to satisfy differential privacy, we propose CheckDP, the first integrated framework to prove and disprove differential privacy. A novel bidirectional Counterexample-Guided Inductive Synthesis (CEGIS) is developed and embedded in CheckDP, enabling it to simultaneously generate a proof for correct systems, as well as a counterexample for incorrect systems. Lastly, we develop DPGen, an automated synthesizer with customizable utility metrics for differential privacy. DPGen employs a novel approach to generate sketch programs and models the synthesis problem as an optimization problem involving privacy and utility, making it flexible and efficient in generating differentially-private programs with different requirements.
Programming Languages and Systems
This book constitutes the proceedings of the 16th Asian Symposium on Programming Languages and Systems, APLAS 2018, held in Wellington, New Zealand, in December 2018. The 22 papers presented in this volume were carefully reviewed and selected from 51 submissions. They are organized in topical sections named: types; program analysis; tools; functional programs and probabilistic programs; verification; logic; and continuation and model checking.
Differential Privacy and Applications
This book focuses on differential privacy and its application with an emphasis on technical and application aspects. This book also presents the most recent research on differential privacy with a theory perspective. It provides an approachable strategy for researchers and engineers to implement differential privacy in real world applications. Early chapters are focused on two major directions, differentially private data publishing and differentially private data analysis. Data publishing focuses on how to modify the original dataset or the queries with the guarantee of differential privacy. Privacy data analysis concentrates on how to modify the data analysis algorithm to satisfy differential privacy, while retaining a high mining accuracy. The authors also introduce several applications in real world applications, including recommender systems and location privacy Advanced level students in computer science and engineering, as well as researchers and professionals working in privacy preserving, data mining, machine learning and data analysis will find this book useful as a reference. Engineers in database, network security, social networks and web services will also find this book useful.