Our open source project on SparkFHE aims to bring fully homomorphic encryption (FHE) to Apache Spark.
Want to learn more about Spark and FHE?
- Apache Spark, a highly efficient data processing framework for the cloud.
- Matei Zaharia, Reynold S. Xin, Patrick Wendell, Tathagata Das, Michael Armbrust, Ankur Dave, Xiangrui Meng, Josh Rosen, Shivaram Venkataraman, Michael J. Franklin, Ali Ghodsi, Joseph Gonzalez, Scott Shenker, Ion Stoica. Apache Spark: A Unified Engine For Big Data Processing. Communications of the ACM, November 2016, Vol. 59 No. 11, Pages 56-65. DOI 10.1145/2934664
- FHE, a cryptography technique that enables arithmetic computations on encrypted data without decrypting it first.
- Daniele Micciancio. Technical Perspective: A First Glimpse of Cryptography's Holy Grail. Communications of the ACM, March 2010, Vol. 53 No. 3, Page 96. DOI 10.1145/1666420.1666445
- Craig Gentry. Computing arbitrary functions of encrypted data. Communications of the ACM. March 2010. DOI 1666420.1666444.
Our project SparkFHE integrates Apache Spark with fully homomorphic encryption – an encryption technology that allows computing directly on encrypted data without requiring a secret key. This integration makes two novel contributions to large-scale secure data analytics in the Cloud: (1) enabling Spark to perform efficient computation on large datasets while preserving user privacy, and (2) accelerating intensive homomorphic computation through parallelization of tasks across clusters of computing nodes. To our best knowledge, SparkFHE is the first addressing these two needs simultaneously. This project is in collaboration with Microsoft Research, Redmond.
Project Source Code and Demo: https://github.com/SpiRITlab/SparkFHE-Examples/wiki
More info on our ongoing projects, please check out https://www.cs.rit.edu/~ph/PrivateComputation