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Sirui Shen

Publications and source records attributed to Sirui Shen.

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Proving Circuit Functional Equivalence in Zero Knowledge

The modern integrated circuit ecosystem is increasingly reliant on third-party intellectual property integration, which introduces security risks, including hardware Trojans and security vulnerabilities. Addressing the resulting trust deadlock between IP vendors and system integrators without exposing proprietary designs requires novel privacy-preserving verification techniques. However, existing privacy-preserving hardware verification methods are all simulation-based and fail to offer formal guarantees. In this paper, we propose ZK-CEC, the first privacy-preserving framework for hardware formal verification. By combining formal verification and zero-knowledge proof (ZKP), ZK-CEC establishes a foundation for formally verifying IP correctness and security without compromising the confidentiality of the designs. We observe that existing zero-knowledge protocols for formal verification are designed to prove statements of public formulas. However, in a privacy-preserving verification context where the formula is secret, these protocols cannot prevent a malicious prover from forging the formula, thereby compromising the soundness of the verification. To address these gaps, we first propose a blueprint for proving the unsatisfiability of a secret design against a public constraint, which is widely applicable to proving properties in software, hardware, and cyber-physical systems. Based on the proposed blueprint, we construct ZK-CEC, which enables a prover to convince the verifier that a secret IP's functionality aligns perfectly with the public specification in zero knowledge, revealing only the length and width of the proof. We implement ZK-CEC and evaluate its performance across various circuits, including arithmetic units and cryptographic components. Experimental results show that ZK-CEC successfully verifies practical designs, such as the AES S-Box, within practical time limits.

cs.CR

Ransomware Negotiation: Dynamics and Privacy-Preserving Mechanism Design

Ransomware attacks have become a pervasive and costly form of cybercrime, causing tens of millions of dollars in losses as organizations increasingly pay ransoms to mitigate operational disruptions and financial risks. While prior research has largely focused on proactive defenses, the post-infection negotiation dynamics between attackers and victims remains underexplored. This paper presents a formal analysis of attacker-victim interactions in modern ransomware incidents using a finite-horizon alternating-offers bargaining game model. Our analysis demonstrates how bargaining alters the optimal strategies of both parties. In practice, incomplete information-attackers lacking knowledge of victims' data valuations and victims lacking knowledge of attackers' reservation ransoms-can prolong negotiations and increase victims' business interruption costs. To address this, we design a Bayesian incentive-compatible mechanism that facilitates rapid agreement on a fair ransom without requiring either party to disclose private valuations. We further implement this mechanism using secure two-party computation based on garbled circuits, thereby eliminating the need for trusted intermediaries and preserving the privacy of both parties throughout the negotiation. To the best of our knowledge, this is the first automated, privacy-preserving negotiation mechanism grounded in a formal analysis of ransomware negotiation dynamics.

cs.GT