{"product_id":"9781617298042","title":"Privacy-Preserving Machine Learning","description":"\u003ctable\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eAuthor\/Contributor(s):\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"\"\u003eChang, J. Morris; Zhuang, Di; Samaraweera , G. Dumindu\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003ePublisher:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eManning\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eDate:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e5\/2\/2023\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eBinding:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"\"\u003ePaperback\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eCondition:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"\"\u003eNEW\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\u003cb\u003e\u003cb\u003eKeep sensitive user data safe and secure without sacrificing the performance and accuracy of your machine learning models.\u003c\/b\u003e\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eIn \u003ci\u003ePrivacy Preserving Machine Learning\u003c\/i\u003e, you will learn:\u003cul\u003e \u003cli\u003ePrivacy considerations in machine learning\u003c\/li\u003e \u003cli\u003eDifferential privacy techniques for machine learning\u003c\/li\u003e \u003cli\u003ePrivacy-preserving synthetic data generation\u003c\/li\u003e \u003cli\u003ePrivacy-enhancing technologies for data mining and database applications\u003c\/li\u003e \u003cli\u003eCompressive privacy for machine learning\u003c\/li\u003e \u003c\/ul\u003e\u003cbr\u003e\u003ci\u003ePrivacy-Preserving Machine Learning\u003c\/i\u003e is a comprehensive guide to avoiding data breaches in your machine learning projects. You’ll get to grips with modern privacy-enhancing techniques such as differential privacy, compressive privacy, and synthetic data generation. Based on years of DARPA-funded cybersecurity research, ML engineers of all skill levels will benefit from incorporating these privacy-preserving practices into their model development. By the time you’re done reading, you’ll be able to create machine learning systems that preserve user privacy without sacrificing data quality and model performance.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the Technology \u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eMachine learning applications need massive amounts of data. It’s up to you to keep the sensitive information in those data sets private and secure. Privacy preservation happens at every point in the ML process, from data collection and ingestion to model development and deployment. This practical book teaches you the skills you’ll need to secure your data pipelines end to end.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the Book \u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003ci\u003ePrivacy-Preserving Machine Learning\u003c\/i\u003e explores privacy preservation techniques through real-world use cases in facial recognition, cloud data storage, and more. You’ll learn about practical implementations you can deploy now, future privacy challenges, and how to adapt existing technologies to your needs. Your new skills build towards a complete security data platform project you’ll develop in the final chapter.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eWhat’s Inside \u003c\/b\u003e\u003cul\u003e \u003cli\u003eDifferential and compressive privacy techniques\u003c\/li\u003e \u003cli\u003ePrivacy for frequency or mean estimation, naive Bayes classifier, and deep learning\u003c\/li\u003e \u003cli\u003ePrivacy-preserving synthetic data generation\u003c\/li\u003e \u003cli\u003eEnhanced privacy for data mining and database applications\u003c\/li\u003e \u003c\/ul\u003e\u003cbr\u003e\u003cb\u003eAbout the Reader\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eFor machine learning engineers and developers. Examples in Python and Java.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the Author \u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eJ. Morris Chang\u003c\/b\u003e is a professor at the University of South Florida. His research projects have been funded by DARPA and the DoD. \u003cb\u003eDi Zhuang\u003c\/b\u003e is a security engineer at Snap Inc. \u003cb\u003eDumindu Samaraweera\u003c\/b\u003e is an assistant research professor at the University of South Florida. The technical editor for this book, \u003cb\u003eWilko Henecka\u003c\/b\u003e, is a senior software engineer at Ambiata where he builds privacy-preserving software.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003ePART 1 - BASICS OF \u003ci\u003ePRIVACY-PRESERVING MACHINE LEARNING\u003c\/i\u003e WITH DIFFERENTIAL PRIVACY\u003c\/b\u003e\u003cbr\u003e1 Privacy considerations in machine learning\u003cbr\u003e2 Differential privacy for machine learning\u003cbr\u003e3 Advanced concepts of differential privacy for machine learning\u003cbr\u003e\u003cb\u003ePART 2 - LOCAL DIFFERENTIAL PRIVACY AND SYNTHETIC DATA GENERATION\u003c\/b\u003e\u003cbr\u003e4 Local differential privacy for machine learning\u003cbr\u003e5 Advanced LDP mechanisms for machine learning\u003cbr\u003e6 Privacy-preserving synthetic data generation\u003cbr\u003e\u003cb\u003ePART 3 - BUILDING PRIVACY-ASSURED MACHINE LEARNING APPLICATIONS\u003c\/b\u003e\u003cbr\u003e7 Privacy-preserving data mining techniques\u003cbr\u003e8 Privacy-preserving data management and operations\u003cbr\u003e9 Compressive privacy for machine learning\u003cbr\u003e10 Putting it all together: Designing a privacy-enhanced platform (DataHub)","brand":"Manning","offers":[{"title":"Default Title","offer_id":48176692560127,"sku":"9781617298042","price":59.99,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0452\/0886\/2873\/files\/Jacket_160bef93-7455-4d4b-95da-3bd8087f543a.jpg?v=1771351510","url":"https:\/\/massivebookshop.com\/products\/9781617298042","provider":"MASSIVE BOOKSHOP","version":"1.0","type":"link"}