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 computationfunctional encryptiongarbled 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},
url = {https://eprint.iacr.org/2020/950}
}
