Protecting Privacy through Homomorphic Encryption

1. Front Matter

   Pages i-xvi

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2. Introduction to Homomorphic Encryption

   1. Front Matter

      Pages 1-1

      PDF

   2. Introduction to Homomorphic Encryption and Schemes

      • Jung Hee Cheon, Anamaria Costache, Radames Cruz Moreno, Wei Dai, Nicolas Gama, Mariya Georgieva et al.

      Pages 3-28

3. Homomorphic Encryption Security Standard

   1. Front Matter

      Pages 29-29

      PDF

   2. Homomorphic Encryption Standard

      • Martin Albrecht, Melissa Chase, Hao Chen, Jintai Ding, Shafi Goldwasser, Sergey Gorbunov et al.

      Pages 31-62

4. Applications of Homomorphic Encryption

   1. Front Matter

      Pages 63-63

      PDF

   2. Privacy-Preserving Data Sharing and Computation Across Multiple Data Providers with Homomorphic Encryption

      • Juan Troncoso-Pastoriza, David Froelicher, Peizhao Hu, Asma Aloufi, Jean-Pierre Hubaux

      Pages 65-80

   3. Secure and Confidential Rule Matching for Network Traffic Analysis

      • Dimitar Jetchev, Alistair Muir

      Pages 81-86

   4. Trusted Monitoring Service (TMS)

      • Xiaoqian Jiang, Miran Kim, Kristin Lauter, Tim Scott, Shayan Shams

      Pages 87-95

   5. Private Set Intersection and Compute

      • Flavio Bergamaschi, Tancrède Lepoint, Peter Leihn, Sreekanth Kannepalli

      Pages 97-104

5. Applications of Homomorphic Encryption

   1. Front Matter

      Pages 105-105

      PDF

   2. Private Outsourced Translation for Medical Data

      • Travis Morrison, Sarah Scheffler, Bijeeta Pal, Alexander Viand

      Pages 107-116

   3. HappyKidz: Privacy Preserving Phone Usage Tracking

      • Benjamin M. Case, Marcella Hastings, Siam Hussain, Monika Trimoska

      Pages 117-127

   4. i-SEAL2: Identifying Spam EmAiL with SEAL

      • I. Demertzis, D. Froelicher, N. Luo, M. Norberg Hovd

      Pages 129-132

   5. PRIORIS: Enabling Secure Detection of Suicidal Ideation from Speech Using Homomorphic Encryption

      • Deepika Natarajan, Anders Dalskov, Daniel Kales, Shabnam Khanna

      Pages 133-146

   6. Gimme That Model!: A Trusted ML Model Trading Protocol

      • Laia Amorós, Syed Mahbub Hafiz, Keewoo Lee, M. Caner Tol

      Pages 147-155

   7. HEalth: Privately Computing on Shared Healthcare Data

      • Leo de Castro, Erin Hales, Mimee Xu

      Pages 157-162

   8. Private Movie Recommendations for Children

      • Anh Pham, Mohammad Samragh, Sameer Wagh, Emily Wenger

      Pages 163-167

   9. Privacy-Preserving Prescription Drug Management Using Fully Homomorphic Encryption

      • Aria Shahverdi, Ni Trieu, Chenkai Weng, William Youmans

      Pages 169-176

6. Correction to: Introduction to Homomorphic Encryption and Schemes

   • Jung Hee Cheon, Anamaria Costache, Radames Cruz Moreno, Wei Dai, Nicolas Gama, Mariya Georgieva et al.

   Pages C1-C1

This book summarizes recent inventions, provides guidelines and recommendations, and demonstrates many practical applications of homomorphic encryption. This collection of papers represents the combined wisdom of the community of leading experts on Homomorphic Encryption. In the past 3 years, a global community consisting of researchers in academia, industry, and government, has been working closely to standardize homomorphic encryption. This is the first publication of whitepapers created by these experts that comprehensively describes the scientific inventions, presents a concrete security analysis, and broadly discusses applicable use scenarios and markets. This book also features a collection of privacy-preserving machine learning applications powered by homomorphic encryption designed by groups of top graduate students worldwide at the Private AI Bootcamp hosted by Microsoft Research.



The volume aims to connect non-expert readers with this important new cryptographic technology in an accessible and actionable way. Readers who have heard good things about homomorphic encryption but are not familiar with the details will find this book full of inspiration. Readers who have preconceived biases based on out-of-date knowledge will see the recent progress made by industrial and academic pioneers on optimizing and standardizing this technology. A clear picture of how homomorphic encryption works, how to use it to solve real-world problems, and how to efficiently strengthen privacy protection, will naturally become clear.

• whitepapers
• privacy protection
• cryptography
• technology developments
• computer science applications
• optimization

“Homomorphic encryption appears as a very acceptable crypto-protection solution with a high level of security, which enables the processing and exchange of data in an encrypted form. … Protecting privacy through homomorphic encryption offers a certain level of help with this, providing readers with basic insights into homomorphic encryption, the problem environment, and certain practical solutions that have proven to be successful in this area.” (F. J. Ruzic, Computing Reviews, June 2, 2023)

• West Coast Research Science, Facebook AI Research, Seattle, USA

  Kristin Lauter

• Cryptography and Privacy Research Group, Microsoft Research, Redmond, USA

  Wei Dai, Kim Laine

Kristin Estella Lauter is an American mathematician and cryptographer particularly known for her work on elliptic curve cryptography, homomorphic encryption, and post-quantum cryptography.  She is currently Head of West Coast Research Labs for FAIR (Facebook AI Research), supervising groups in Core Machine Learning, Computer Vision, Robotics, and Privacy. From 2008—2021 she was a Principal Researcher and Partner Research Manager of the Cryptography and Privacy Group at Microsoft Research in Redmond, Washington, which developed Microsoft SEAL. Lauter was President of the Association for Women in Mathematics (AWM) from 2015—2017.  She is an Elected Fellow of the American Mathematical Society (2015), AWM (2017), the  Society of Industrial and Applied Mathematics (2020), and the American Association for the Advancement of Science (2021). She was the Polya Lecturer for the Mathematical Association of America for 2018—2020.  Lauter earned all her degrees in mathematics from the University of Chicago, in BA (1990),  MS (1991), and PhD (1996). Prior to joining Microsoft, she was Hildebrandt Research Assistant Professor at the University of Michigan (1996-1999), Visiting Scholar at Max Planck Institut fur Mathematik in Bonn, Germany (1997), and a Visiting Researcher at Institut de Mathematiques Luminy in France (1999). She is a co-founder of the HomomorphicEncryption.org community and a Steering Committee member. 



Wei Dai is a senior research SDE in the Cryptography and Privacy Research group at Microsoft Research. He received a PhD degree in Electrical and Computer Engineering from Worcester Polytechnic Institute in 2019. His research interests include applied cryptography, privacy-enhancing technologies, and cryptographic implementations. Wei is a contributor to the homomorphic encryption library Microsoft SEAL and leads the implementation of homomorphic encryption on alternative hardware platforms.



Kim Laine is a principal researcher and research manager of the Cryptography and Privacy Research Group at Microsoft Research, Redmond. He holds a PhD in mathematics from UC Berkeley and since graduation has been working at Microsoft Research on applied cryptography and privacy-enhancing technologies. Kim’s contributions to homomorphic encryption range from academic research to development of the homomorphic encryption library Microsoft SEAL. He is a co-founder of the HomomorphicEncryption.org community and a Steering Committee member.