A hardware signature for non-manipulable digital sensor readout
Publication Date: 2018-Apr-20
The IP.com Prior Art Database
Fully calibrated sensors with digital communication interfaces can be integrated into digital communication networks, allowing even remote users to retrieve sensor data via the network. Thereby, sensor data is transmitted along the network path from node to node, before reaching the end user. If data is transmitted in plain text without inclusion of a digital signature of the sensor data, the end user cannot be certain that the data has not been manipulated during the data transfer through one of the nodes, or even by a compromised sensor software itself. We propose a "safe-sensor" concept describing how to make sure that transmitted sensor data, received by an end user, is certified: A) By sensor identity – the sensor is proven to be the sensor of interest with the correct sensor ID and manufactured by the company of interest. B) By proof of measurement event – the end user can be certain that the corresponding sensor measurement has been conducted within a given timeframe. C) By sensor-data integrity – the sensor data can be proven to remain intact along the path through the network. We propose a way to implement this safe-sensor concept by using an asymmetrical-cryptographic algorithm as a hardware-implemented digital-ASIC block integrated between the digital (calibrated) sensor output and the digital user interface of the chip. Safe sensors could be used to certify the sensor identity (e.g. sensor manufacturing company and other data), preventing fraudulent competitors to sell inferior sensor forgery under the name of the original manufacturer. In general, all measurement applications, which require tamperproof data collection and transmission to the end user, could make use of safe sensors. Especially, critical applications subject to emission regulations, such as monitoring of radiation or hazardous chemical vapors, could require safe sensors. Moreover, certified sensor-data readout could be used by logistics companies, e.g. to certify the maintenance of a cool chain during the transport of sensitive goods. Another use case would be the certified monitoring of natural-gas consumption in homes using smart gas meters (with integrated safe gas-flow sensors), preventing the fraudulent manipulation of sensor data throughout the whole communication chain from consumer to gas provider. Moreover, safe sensors could be used for certified tracing of sensor data in artificial-intelligence or autonomous systems (like self-driving cars or autonomous medical-monitoring systems in hospitals), even for past events (e.g. accidents). Another interesting use case would be a certified sensor readout in large sensor networks or even sensor-data marketplaces, in which data, e.g. measured by sensors in mobile devices, has a commercial value.