Remote IoT devices face significant security risks due to their inherent physical vulnerability. An adversarial actor with sufficient capability can monitor the devices or exfiltrate data to access sensitive information. Remotely deployed devices such as sensors need enhanced resilience against memory leakage if performing privileged tasks. To increase the security and trust of these devices we present a novel framework implementing a privacy homomorphism which creates sensor data directly in an encoded format. The sensor data is permuted at the time of creation in a manner which appears random to an observer. A separate secure server in communication with the device provides necessary information which allows the device to perform processing on the encoded data but does not allow decoding of the result. The device transmits the encoded results to the secure server which maintains the ability to interpret the results. In this article, we show how this framework works for an image sensor calculating differences between a stream of images, with initial results showing an overhead as low as only 266% in terms of throughput when compared to computing on standard unencoded numbers such as two’s complement. We further show 5,000× speedup over a recent homomorphic encryption ASIC.

Deployed embedded devices face security risks due to increased ease of physical access to the devices by unauthorized users. Capable adversaries can intercept a device to recover the data in memory, including results of performed sensitive computations. Device owners require data confidentiality on their physically insecure devices. To satisfy this goal we implement a novel method, COPPER (Computation Obfuscation by Producing Permutations for Encoding Randomly), to create data which never exists on the device digitally in plaintext format and which is subsequently used for computation. In this paper we utilize COPPER to calculate a moving average computation on encoded data.1