Session Chair: Kimia Zamiri Azar, University of Florida

Wednesday, June 29, 2022 | Time: 11:10 - 12:10

Location: Int'l Ballroom C

• 41. POCA: First Power-on Chip Authentication in Untrusted Foundry and Assembly
  Md Sami Ul Islam Sami, Fahim Rahman, Adam Cron, Dale Donchin, Mike Borza, Farimah Farahmandi and Mark Tehranipoor
  Abstract: The increased complexity, extensive verification requirement, shortened time-to-market, and increased manufacturing and test costs have made fabless design houses adopt the horizontal business model where system-on-chips (SoCs) are shipped to outsourced semiconductor assembly and test facilities. However, such a model renders offshore facilities complete control over the manufacturing and test of integrated circuits (ICs), potentially enabling them to perform attacks like IC overproduction, intellectual property (IP) piracy, shipping defective chips into the supply chain, and stealing security assets (e.g., locking keys). In this paper, we propose a novel protocol called POCA, enabling the first power-on chip authentication during wafer sort to securely provision design assets inside the chip. Using POCA, the design house can authenticate the chips on the untrusted manufacturing/test floor and generate a shared secret key with the chip. This key is then utilized to encrypt the secret assets and securely provision them inside the chip after decryption. To the best of our knowledge, POCA is the first protocol that performs authentication at an untrusted foundry and ensures secure communication with the chip during the test. POCA has been implemented for ASIC and FPGA environments and is proven resistant to all possible attacks known to us as of today.


• 87. RUDBA: Reusable User-Device Biometric Authentication Scheme for Multi-service Systems
  Zhonghao Liao and Yong Guan
  Abstract: The authentication and verification of user and device identities require cost-effective solutions. Two emerging approaches, biometric authentication, and devices’ fingerprint, allow users and devices to prove their identity efficiently and securely. Furthermore, users are inclined to register multiple services with the same secret information. Therefore, the potential risks brought by the reuse of secret information need to be taken seriously. This paper proposes the RUDBA scheme, a novel reusable user-device biometric authentication scheme that captures the user’s biometrics and the device’s fingerprint. The extracted confidential is fused as authentication information for the user-device pair’s identity and can provide a symmetric key for the subsequent communication. This scheme is implemented using the public biometric dataset and the intrinsic SRAM PUF data. The experimental results and analysis show that the RUDBA scheme leads to a reliable and reusable users-device authentication system.


• 45. Unclonable Optical Identity for Universal Product Verification
  Chenxing Wang, Lily Raymond, Yifei Jin, Alireza Tavakkoli and Haoting Shen
  Abstract: Reliable identity(ID) is the cornerstone of product verification for supply chain trust and security. Traditional popular ID techniques, such as serials numbers and bar codes, can be either easily cloned. While novel ID techniques, such as the ones based on physical unclonable functions (PUFs) or nano-chemicals, exploit the randomness in micro/nano-scale to make the cloning difficult. However, the PUF-based ID is applicable only on electronics and the nano-chemical ID usually requires either specific fabrications or inconvenient verification processes. To address these shortcomings, we propose a novel 3D ID tag with random micro- structure features, preventing the cloning through the technical difficulties of the structure reproducing. The proposed ID can be produced in a cost efficient way, applied on most products with a solid surface, and verified conveniently by cell-phone level equipment. In this paper, we introduce the design and the fabrication of our tag. Then we take the tag’s optical images and develop the verification algorithm enhanced by a machine learning-based object detection. Experimental results from our 3D tag prototypes demonstrate the reliability of the verification.