Differential privacy and applications

Description

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. Read more... Abstract: 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. Read more... Front Matter ....Pages i-xiii Introduction (Tianqing Zhu, Gang Li, Wanlei Zhou, Philip S. Yu)....Pages 1-6 Preliminary of Differential Privacy (Tianqing Zhu, Gang Li, Wanlei Zhou, Philip S. Yu)....Pages 7-16 Differentially Private Data Publishing: Settings and Mechanisms (Tianqing Zhu, Gang Li, Wanlei Zhou, Philip S. Yu)....Pages 17-21 Differentially Private Data Publishing: Interactive Setting (Tianqing Zhu, Gang Li, Wanlei Zhou, Philip S. Yu)....Pages 23-34 Differentially Private Data Publishing: Non-interactive Setting (Tianqing Zhu, Gang Li, Wanlei Zhou, Philip S. Yu)....Pages 35-48 Differentially Private Data Analysis (Tianqing Zhu, Gang Li, Wanlei Zhou, Philip S. Yu)....Pages 49-65 Differentially Private Deep Learning (Tianqing Zhu, Gang Li, Wanlei Zhou, Philip S. Yu)....Pages 67-82 Differentially Private Applications: Where to Start? (Tianqing Zhu, Gang Li, Wanlei Zhou, Philip S. Yu)....Pages 83-90 Differentially Private Social Network Data Publishing (Tianqing Zhu, Gang Li, Wanlei Zhou, Philip S. Yu)....Pages 91-105 Differentially Private Recommender System (Tianqing Zhu, Gang Li, Wanlei Zhou, Philip S. Yu)....Pages 107-129 Privacy Preserving for Tagging Recommender Systems (Tianqing Zhu, Gang Li, Wanlei Zhou, Philip S. Yu)....Pages 131-150 Differentially Location Privacy (Tianqing Zhu, Gang Li, Wanlei Zhou, Philip S. Yu)....Pages 151-172 Differentially Private Spatial Crowdsourcing (Tianqing Zhu, Gang Li, Wanlei Zhou, Philip S. Yu)....Pages 173-189 Correlated Differential Privacy for Non-IID Datasets (Tianqing Zhu, Gang Li, Wanlei Zhou, Philip S. Yu)....Pages 191-214 Future Directions and Conclusion (Tianqing Zhu, Gang Li, Wanlei Zhou, Philip S. Yu)....Pages 215-222 Back Matter ....Pages 223-235