Homomorphic Encryption for Data Science (HE4DS) | Agenda Bookshop Skip to content
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A01=Allon Adir
A01=Ehud Aharoni
A01=Hayim Shaul
A01=Nir Drucker
A01=Omri Soceanu
A01=Ronen Levy
Age Group_Uncategorized
Age Group_Uncategorized
Author_Allon Adir
Author_Ehud Aharoni
Author_Hayim Shaul
Author_Nir Drucker
Author_Omri Soceanu
Author_Ronen Levy
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Category1=Non-Fiction
Category=GPJ
Category=URD
Category=URY
Category=UTN
Category=UYQM
COP=Switzerland
Delivery_Pre-order
Language_English
PA=Not yet available
Price_€50 to €100
PS=Forthcoming
softlaunch

Homomorphic Encryption for Data Science (HE4DS)

This book provides basic knowledge required by an application developer to understand and use the Fully Homomorphic Encryption (FHE) technology for privacy preserving Data-Science applications. The authors present various techniques to leverage the unique features of FHE and to overcome its characteristic limitations.

Specifically, this book summarizes polynomial approximation techniques used by FHE applications and various data packing schemes based on a data structure called tile tensors, and demonstrates how to use the studied techniques in several specific privacy preserving applications. Examples and exercises are also included throughout this book.

The proliferation of practical FHE technology has triggered a wide interest in the field and a common wish to experience and understand it. This book aims to simplify the FHE world for those who are interested in privacy preserving data science tasks, and for an audience that does not necessarily have a deep cryptographic background, including undergraduate and graduate-level students in computer science, and data scientists who plan to work on private data and models.

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Current price €70.19
Original price €77.99
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A01=Allon AdirA01=Ehud AharoniA01=Hayim ShaulA01=Nir DruckerA01=Omri SoceanuA01=Ronen LevyAge Group_UncategorizedAuthor_Allon AdirAuthor_Ehud AharoniAuthor_Hayim ShaulAuthor_Nir DruckerAuthor_Omri SoceanuAuthor_Ronen Levyautomatic-updateCategory1=Non-FictionCategory=GPJCategory=URDCategory=URYCategory=UTNCategory=UYQMCOP=SwitzerlandDelivery_Pre-orderLanguage_EnglishPA=Not yet availablePrice_€50 to €100PS=Forthcomingsoftlaunch

Will deliver when available. Publication date 15 Dec 2024

Product Details
  • Dimensions: 155 x 235mm
  • Publication Date: 15 Dec 2024
  • Publisher: Springer International Publishing AG
  • Publication City/Country: Switzerland
  • Language: English
  • ISBN13: 9783031654930

About Allon AdirEhud AharoniHayim ShaulNir DruckerOmri SoceanuRonen Levy

Allon Adir holds an M.Sc. in Computer Science from the Technion - Israel Institute of Technology and is now a researcher in the AI Security group at IBM Research - Israel in Haifa. Allon worked on research related to hardware verification and then on applications of analytics to Cyber-security. Allon is currently working on novel encryption schemes and their application in the context of security and privacy preservation. Allon has authored many publications and patents related to the above fields and is an IBM Master Inventor. Ehud is currently with IBM Research in Haifa Israel. Ehud received an M.Sc. in computer science from the Technion - Israel Institute of Technology. He worked several years on machine learning projects in the fields of hardware verification healthcare and anomaly detection for computer systems. Later he worked on various applications of machine learning to cyber security. Ehud is currently working on novel encryption schemes in the context of security and privacy preservation. Nir Drucker is a Security Privacy & Cryptography Research Scientist at IBM Research - Israel the AI Security group that develop the IBM HElayers SDK. He holds a Ph.D. in Applied Mathematics (Cryptography) from the University of Haifa and an M.Sc. degree in Operations Research from the faculty of Industrial Engineering & Management of the Technion I.I.T. Nir worked 3.5 years as a Senior Applied Scientist in AWS the Cryptographic Algorithms Team and eight years as a Software Developer at Intel in two teams: a) a team that developed low-level security features (e.g. SGX) in FW/SW; b) a team that developed a CAD VLSI timing verification simulator in C/C++. His research interests involve applied cryptography and applied security. In particular research that combines these domains with the latest development in the machine-learning field. For example researching Privacy-Preserving Machine Learning (PPML) solutions that involve Homomorphic Encryption (HE) or multi party computation (MPC). Ronen Levy is a senior manager leading the Security & Privacy department in the IBM Research - Israel lab also responsible for the IBM Research strategy around Data Security. He holds a B.A in Mathematics from the University of Haifa and with over 30 years of experience in R&D he has been driving innovation in various domains such as hybrid-cloud software quality privacy cyber-security and cryptography. Before joining IBM Research 14 years ago he worked in various R&D development roles in the industry developing products such as Anti-Virus Distributed Query Processor Application Server and Enterprise Storage System. Hayim is currently with IBM Research in Haifa Israel. Hayim completed his PhD in computational geometry under the supervision of Prof. Micha Sharir in Tel- Aviv University. Following his studies Hayim cofounded and served as CTO of DiviNetworks a company funded by the IFC that focused on network optimizations. After that he joined MIT as a research fellow in the CSAIL lab doing research in homomorphic encryption. At IBM Research Hayim is continuing his work on secure multi-party computation. Omri Soceanu is the head of the AI Security research group at IBM Research Haifa. He holds a B.Sc. and M.Sc. in Electrical Engineering from the Technion - Israel Institute of Technology. Before becoming the head of the AI Security group Omri worked on various aspects of data security employing machine learning techniques in a Big Data setting using state-of-the-art approaches. Omri has several years of hands-on experience working on embedded systems cryptography cybersecurity and machine learning algorithms.

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