Self-Processing Private Sensor Data via Garbled Encryption

Authors: Nathan Manohar (UCLA), Abhishek Jain (John Hopkins University), Amit Sahai (UCLA)

Volume: 2020
Issue: 4
Pages: 434–460

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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.

Keywords: Secure computation, Functional encryption, Garbled circuits

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