Paper 2020/950

Self-Processing Private Sensor Data via Garbled Encryption

Nathan Manohar, Abhishek Jain, and Amit Sahai


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.

Available format(s)
Cryptographic protocols
Publication info
Published elsewhere. PETS 2020
Secure computationfunctional encryptiongarbled circuits
Contact author(s)
nmanohar @ cs ucla edu
abhishek @ cs jhu edu
sahai @ cs ucla edu
2020-08-11: received
Short URL
Creative Commons Attribution


      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{}},
      url = {}
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