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1. Physical Unclonable Function (PUF)-Based e-Cash Transaction Protocol (PUF-Cash)

   https://doi.org/10.3390/cryptography3030018  

   Calhoun, Jeff ; Minwalla, Cyrus ; Helmich, Charles ; Saqib, Fareena ; Che, Wenjie ; Plusquellic, Jim ( September 2019 , Cryptography)

   null (Ed.)

   Electronic money (e-money or e-Cash) is the digital representation of physical banknotes augmented by added use cases of online and remote payments. This paper presents a novel, anonymous e-money transaction protocol, built based on physical unclonable functions (PUFs), titled PUF-Cash. PUF-Cash preserves user anonymity while enabling both offline and online transaction capability. The PUF’s privacy-preserving property is leveraged to create blinded tokens for transaction anonymity while its hardware-based challenge–response pair authentication scheme provides a secure solution that is impervious to typical protocol attacks. The scheme is inspired from Chaum’s Digicash work in the 1980s and subsequent improvements. Unlike Chaum’s scheme, which relies on Rivest, Shamir and Adlemans’s (RSA’s) multiplicative homomorphic property to provide anonymity, the anonymity scheme proposed in this paper leverages the random and unique statistical properties of synthesized integrated circuits. PUF-Cash is implemented and demonstrated using a set of Xilinx Zynq Field Programmable Gate Arrays (FPGAs). Experimental results suggest that the hardware footprint of the solution is small, and the transaction rate is suitable for large-scale applications. An in-depth security analysis suggests that the solution possesses excellent statistical qualities in the generated authentication and encryption keys, and it is robust against a variety of attack vectors including model-building, impersonation, and side-channel variants. 

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2. Physical Unclonable Function (PUF)-based e-Cash Transaction Protocol (PUF-Cash)

   Jeff Calhoun, Cyrus Minwalla ( January 2019 , Cryptography)

   Electronic money (e‐money or e‐Cash) is the digital representation of physical banknotes augmented by added use cases of online and remote payments. This paper presents a novel, anonymous e‐money transaction protocol, built based on physical unclonable functions (PUFs), titled PUF‐Cash. PUF‐Cash preserves user anonymity while enabling both offline and online transaction capability. The PUF’s privacy‐preserving property is leveraged to create blinded tokens for transaction anonymity while its hardware‐based challenge–response pair authentication scheme provides a secure solution that is impervious to typical protocol attacks. The scheme is inspired from Chaum’s Digicash work in the 1980s and subsequent improvements. Unlike Chaum’s scheme, which relies on Rivest, Shamir and Adlemans’s (RSA’s) multiplicative homomorphic property to provide anonymity, the anonymity scheme proposed in this paper leverages the random and unique statistical properties of synthesized integrated circuits. PUF‐Cash is implemented and demonstrated using a set of Xilinx Zynq Field Programmable Gate Arrays (FPGAs). Experimental results suggest that the hardware footprint of the solution is small, and the transaction rate is suitable for large‐scale applications. An in‐depth security analysis suggests that the solution possesses excellent statistical qualities in the generated authentication and encryption keys, and it is robust against a variety of attack vectors including model‐building, impersonation, and side‐ channel variants. 

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3. Reinforcement Learning Toward Decision-Making for Multiple Trusted-Third-Parties in PUF-Cash

   https://doi.org/10.1109/WF-IoT48130.2020.9221344  

   Fragkos, Georgios ; Minwalla, Cyrus ; Plusquellic, Jim ; Tsiropoulou, Eirini Eleni ( June 2020 , IEEE 6th World Forum on Internet of Things (WF-IoT))

   null (Ed.)

   Electronic money is the digital representation of physical banknotes enabling offline and online payments. An electronic e-Cash scheme, termed PUF- Cash was proposed in prior work. PUF-Cash preserves user anonymity by leveraging the random and unique statistical properties of physically unclonable functions (PUFs). PUF-Cash is extended meaningfully in this work by the introduction of multiple trusted third parties (TTPs) for token blinding and a fractional scheme to diversify and mask Alice's spending habits from the Bank. A reinforcement learning (RL) framework based on stochastic learning automata (SLA) is proposed to efficiently select a subset of TTPs as well as the fractional amounts for blinding per TTP, based on the set of available TTPs, the computational load per TTP and network conditions. An experimental model was constructed in MATLAB with multiple TTPs to verify the learning framework. Results indicate that the RL approach guarantees fast convergence to an efficient selection of TTPs and allocation of fractional amounts in terms of perceived reward for the end-users. 

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4. Divisible E-Cash for Billing in Private Ad Retargeting

   https://doi.org/10.56553/popets-2024-0104  

   Liao, Kevin ; Corrigan-Gibbs, Henry ; Boneh, Dan ( October 2024 , Proceedings on Privacy Enhancing Technologies)

   This paper presents new techniques for private billing in systems for privacy-preserving online advertising. In particular, we show how an ad exchange can use an e-cash scheme to bill advertisers for ad impressions without learning which client saw which ad: The exchange issues electronic coins to advertisers, advertisers pay publishers (via clients) for ad impressions, and publishers unlinkably redeem coins with the exchange. To implement this proposal, we design a new divisible e-cash scheme that uses modern zero-knowledge proofs to reduce the ad exchange's computational costs by roughly 250x compared to the previous state-of-the-art. With our new e-cash scheme, our private-billing infrastructure adds little overhead to existing private ad-retargeting systems: less than 63 ms of latency, negligible client computation, less than 3.2 KB of client communication, and a combined server operating cost (advertisers, publishers, and exchange) of less than 1% of ad spend, an over 5x savings compared to the previous state-of-the-art. 

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5. SPRITE: Secure and Private Routing in Payment Channel Networks

   Panwar, Gaurav ; Vishwanathan, Roopa ; Torres, George ; Misra Satyajayant ( July 2024 , ACM Asia Conference on Computer and Communications Security (ACM AsiaCCS))

   Payment channel networks are a promising solution to the scalability challenge of blockchains and are designed for significantly increased transaction throughput compared to the layer one blockchain. Since payment channel networks are essentially decentralized peerto- peer networks, routing transactions is a fundamental challenge. Payment channel networks have some unique security and privacy requirements that make pathfinding challenging, for instance, network topology is not publicly known, and sender/receiver privacy should be preserved, in addition to providing atomicity guarantees for payments. In this paper, we present an efficient privacypreserving routing protocol, SPRITE, for payment channel networks that supports concurrent transactions. By finding paths offline and processing transactions online, SPRITE can process transactions in just two rounds, which is more efficient compared to prior work. We evaluate SPRITE’s performance using Lightning Network data and prove its security using the Universal Composability framework. In contrast to the current cutting-edge methods that achieve rapid transactions, our approach significantly reduces the message complexity of the system by 3 orders of magnitude while maintaining similar latencies. 

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