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商品描述
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.
商品描述(中文翻譯)
本書專注於差分隱私及其應用，強調技術和應用方面。本書還展示了差分隱私的最新研究，並從理論的角度進行探討。它為研究人員和工程師提供了一種可行的策略，以在現實世界的應用中實現差分隱私。
早期章節集中於兩個主要方向：差分隱私數據發布和差分隱私數據分析。數據發布專注於如何在保證差分隱私的情況下修改原始數據集或查詢。隱私數據分析則集中於如何修改數據分析算法以滿足差分隱私的要求，同時保持高挖掘準確性。作者還介紹了幾個現實世界應用中的案例，包括推薦系統和位置隱私。
計算機科學和工程的高級學生，以及從事隱私保護、數據挖掘、機器學習和數據分析的研究人員和專業人士，將會發現本書作為參考資料非常有用。從事數據庫、網絡安全、社交網絡和網絡服務的工程師也會覺得本書有價值。
