Journal
IEEE INTERNET OF THINGS JOURNAL
Volume 6, Issue 1, Pages 335-348Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2018.2837751
Keywords
Error correcting codes; fuzzy commitment; noisy source data; physical unclonable function (PUF)
Categories
Funding
- Institute for Information and Communications Technology Promotion through the Study on Secure Key Hiding Technology for IoT Devices (KeyHAS Project) [2016-0-00399]
- National Research Foundation of Korea through the Korea Government [2018R1A2B6006903]
- National Research Foundation of Korea [2018R1A2B6006903] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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To create an environment for IoT devices, securely, it is necessary to establish a cryptographic key for those devices. Conventionally, this key has been stored on the actual device, but this leaves the key vulnerable to physical attacks in the IoT environment. To solve this problem, several research studies have been conducted on how best to conceal the cryptographic key. Recently, these studies have most often focused on generating the key dynamically from noisy data using a fuzzy extractor or providing secure storage using a fuzzy commitment. Thus, far, all of these studies use only one type of noisy source data, such as biometric data or physical unclonable function (PUF). However, since most IoT devices are operated in unmanned environments, where biometric data is unavailable, the method using biometric data cannot be utilized for unmanned IoT devices. Although the method using PUF is applied to these unmanned devices, these are still vulnerable against physical attacks including unintended move or theft. In this paper, we present a novel way to use the fuzzy commitment on such devices, called two-factor fuzzy commitment scheme. The proposed method utilizes two noisy factors from the inside and outside of the IoT device. Therefore, although an attacker acquiring the IoT device can access the internal noisy source, the attacker cannot extract the right key from that information only. We also give a prototype implementation for ensuring the feasibility of our two-factor fuzzy commitment concept by utilizing the image data and PUF data for two noisy factors.
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