Aarhus University Seal

Trident: Efficient 4PC Framework for Privacy Preserving Machine Learning

Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearchpeer-review

  • Sai Rahul Rachuri
  • ,
  • Ajith Suresh, Indian Institute of Science, India
  • Harsh Chaudhari, Indian Institute of Science, India
Machine learning has started to be deployed infields such as healthcare and finance, which involves dealing with a lot of sensitive data. This propelled the need for and growth of privacy-preserving machine learning (PPML). We propose an actively secure four-party protocol (4PC), and a framework for PPML, showcasing its applications on four of the most widely-known machine learning algorithms – Linear Regression,Logistic Regression, Neural Networks, and Convolutional Neural Networks.Our 4PC protocol tolerating at most one malicious corruption is practically efficient as compared to Gordon et al. (ASIACRYPT2018) as the 4th party in our protocol is not active in the online phase, except input sharing and output reconstruction stages.Concretely, we reduce the online communication as compared to them by 1 ring element. We use the protocol to build an efficient mixed-world framework (Trident) to switch between the Arithmetic, Boolean, and Garbled worlds. Our framework operates in the offline-online paradigm over rings and is instantiated inan outsourced setting for machine learning, where the data is secretly shared among the servers. Also, we propose conversions especially relevant to privacy-preserving machine learning. With the privilege of having an extra honest party, we outperform the current state-of-the-art ABY3 (for three parties), in terms of both rounds as well as communication complexity.The highlights of our framework include using a minimal number of expensive circuits overall as compared to ABY3. This can be seen in our technique for truncation, which does not affect the online cost of multiplication and removes the need for any circuits in the offline phase. Our B2A conversion has an improvement of 7× in rounds and 18× in the communication complexity. In addition to these, all of the special conversions for machine learning, e.g. Secure Comparison, achieve constant round complexity.The practicality of our framework is argued through improvements in the bench marking of the aforementioned algorithms when compared with ABY3. All the protocols are implemented over a 64-bit ring in both LAN and WAN settings. Our improvements go up to 187× for the training phase and 158× for the prediction phase when observed over LAN and WAN.
Original languageEnglish
Title of host publicationProceedings 2020 Network and Distributed System Security Symposium
Number of pages18
Place of publicationReston
PublisherInternet Society
Publication year2020
ISBN (Electronic)1-891562-61-4
Publication statusPublished - 2020
EventNetwork and Distributed System Security Symposium - San Diego, United States
Duration: 23 Feb 202026 Feb 2020


ConferenceNetwork and Distributed System Security Symposium
LandUnited States
BySan Diego

See relations at Aarhus University Citationformats

ID: 199065295