Self-Processing Private Sensor Data via Garbled Encryption

Nathan Manohar; Abhishek Jain; Amit Sahai

Paper 2020/950

Self-Processing Private Sensor Data via Garbled Encryption

Nathan Manohar, Abhishek Jain, and Amit Sahai

Abstract

We introduce garbled encryption, a relaxation of secret-key multi-input functional encryption (MiFE) where a function key can be used to jointly compute upon only a particular subset of all possible tuples of ciphertexts. We construct garbled encryption for general functionalities based on one-way functions. We show that garbled encryption can be used to build a self-processing private sensor data system where after a one-time trusted setup phase, sensors deployed in the field can periodically broadcast encrypted readings of private data that can be computed upon by anyone holding function keys to learn processed output, without any interaction. Such a system can be used to periodically check, e.g., whether a cluster of servers are in an "alarm" state. We implement our garbled encryption scheme and find that it performs quite well, with function evaluations in the microseconds. The performance of our scheme was tested on a standard commodity laptop.

Metadata

Available format(s): PDF
Category: Cryptographic protocols
Publication info: Published elsewhere. PETS 2020
Keywords: Secure computation functional encryption garbled circuits
Contact author(s): nmanohar @ cs ucla edu
abhishek @ cs jhu edu
sahai @ cs ucla edu
History: 2020-08-11: received
Short URL: https://ia.cr/2020/950
License: CC BY

BibTeX

@misc{cryptoeprint:2020/950,
      author = {Nathan Manohar and Abhishek Jain and Amit Sahai},
      title = {Self-Processing Private Sensor Data via Garbled Encryption},
      howpublished = {Cryptology ePrint Archive, Paper 2020/950},
      year = {2020},
      note = {\url{https://eprint.iacr.org/2020/950}},
      url = {https://eprint.iacr.org/2020/950}
}