This paper introduces a novel PPCC method based on post-quantum fully homomorphic encryption that enables token-based replay fitness computation entirely in the encrypted domain using post-quantum FHE, and is the first method that enables token-based replay fitness computation entirely in the encrypted domain using post-quantum FHE.
Abstract
Conformance checking is a fundamental task in process mining that evaluates how well the observed executions recorded in event logs conform to a given process model. This task enables the identification of deviations, inefficiencies, and bottlenecks in real-world business processes, thereby supporting process improvement and compliance analysis. However, when business processes are deployed in untrusted environments, outsourcing both event logs and process models introduces significant privacy risks, as sensitive operational information may be exposed to external service providers. Existing privacy-preserving conformance checking (PPCC) approaches remain limited, as they primarily rely on partial anonymization, secure multiparty computation, or lightweight encryption mechanisms, without enabling computation directly over encrypted data. This paper introduces a novel PPCC method based on post-quantum fully homomorphic encryption (FHE). The proposed method performs token-based replay fitness evaluation directly over encrypted process models and encrypted event logs. Specifically, the method incorporates a privacy-preserving token-based replay mechanism that homomorphically evaluates encrypted Petri net models and encrypted event logs, enabling the fitness value to be obtained while preserving data confidentiality throughout the computation process. To the best of our knowledge, this is the first method that enables token-based replay fitness computation entirely in the encrypted domain using post-quantum FHE. An experimental evaluation was conducted using synthetic and real-life event logs to analyze the proposed method in terms of utility preservation, computational performance and scalability. The results demonstrate that the encrypted-domain fitness computation produces results equivalent to those obtained in the plaintext setting, while introducing manageable computational overhead. By combining formal conformance checking techniques with quantum-resistant cryptography, the proposed method enables privacy-preserving fitness analysis under outsourced and untrusted environments.
As the cloud computing and mass data sharing develop, data integrity and privacy has become an imperativeissue. Conventional remote data auditing techniques tend to reveal sensitive data or they have high computational cost.In order to overcome these shortcomings, the Fully Homomorphic Encryption enhanced Remote Method Invocation(FHEbRMI) mechanism that includes a combination of the Modified Least Squares (MLS) optimization model and theproposed cloud auditing security and efficiency are proposed in this paper. The suggested system provides an encrypteddata auditing system, which involves RMI-based communication, to enable the client, server, and third-party auditor toperform their verification functions remotely without the disclosure of the plaintext data. An actual execution of thesuggested structure is introduced, such as secure key generation, trapdoor-based dimensionality reduction, ciphertextmultiplication, and optimized homomorphic functions. Moreover, the RMI interface provides a smooth communicationamong the distributed nodes and increases the scalability and minimizes transmission delays. A comparative study withthe recent homomorphic-based auditing schemes like blockchain-assisted, certificateless and lattice-based FHE modelreveals that the proposed FHEbRMI-MLS model has better performance in terms of encryption/decryption latency,computational cost, and encryption overhead. The experimental performance is indicative of an average 37 and 42factor in speed of encryption and enhancement of computational efficiency respectively with respect to the traditionalFHE models. This paper presents a viable, privacy-friendly auditing framework of clouds which guarantees the end-toend encrypted verification without sacrificing the efficiency.
Deepshikha Chaturvedi, Vidyullata Devmane, Shashikant S. Radke et al.· International Journal of Com...· 0 citations
Cyber–physical systems (CPSs) increasingly rely on complex software components whose vulnerabilities may affect both digital services and physical processes. Fuzzing is a practical technique for discovering such vulnerabilities in CPS-facing parsers, protocol handlers, and edge services. Distributed fuzzing improves throughput, but outsourcing fuzzing tasks to multiple untrusted nodes introduces privacy risks: valuable seeds, especially crash-triggering samples, may reveal vulnerability information before affected users are protected. In this paper, we propose PrivFuzz, a privacy-preserving collaborative fuzzing framework. PrivFuzz allows organizations and individuals to collaborate and receive rewards while keeping fuzzing seeds confidential and enabling controlled encrypted seed reuse among untrusted fuzzing nodes. The key idea is to combine trusted execution environments (TEEs) with blockchain-based smart contracts to support confidentiality and fair reward settlement. We give game-based definitions and reduction-style arguments for seed confidentiality, worker soundness, outsourcer atomicity, and duplicate-claim resistance under an attested execution model. We implement a PrivFuzz prototype and evaluate it on four open-source parsing targets. Separately, native AFL++ sanity checks suggest that CPS-facing industrial protocol parsers such as Modbus and OPC UA fall within the same fuzzable target domain. Demonstrating end-to-end PrivFuzz on CPS control programs is left as future work. Using PrivFuzz, we discovered nine bugs and reported them to the developers.
Zhe Chen, Xiaohan Zhang, Ning Zhang et al.· Electronics· 0 citations
This research introduces a novel Unified Quantum-Resilient Blockchain-Zero Knowledge Proofs Privacy Authentication Framework (QBC-ZKPAF) aimed at enhancing security in IoT environments. The system combines post-quantum cryptography, blockchain technology, and Zero Trust Architecture (ZTA) to provide secure communication, access management, and privacy-preserving authentication. It uses a Deep Q-Network Multi-Factor safe Key (DQN-MFSK) for dynamic key selection, a hybrid Reinforcement-Lattice Blockchain Key Generation for quantum-resilient key creation, and Zero-Knowledge Proofs for privacy-preserving signatures to ensure a safe Internet of Things environment. Data privacy, secrecy, auditability, traceability, and resistance to changing threats, such as quantum attacks, are all guaranteed by this architecture. Transparency and thorough post-event audit trails are supported by the blockchain ledger's immutability, which records all access attempts, data exchanges, and device interactions in an unchangeable way. Through a tracing key kept on the audit server within the Zero Trust Architecture, the architecture allows accurate source tracing in the event of suspicious activity or breaches. QBC-ZKPAF provides strong security and privacy solutions for Internet of Things networks by adopting multi-factor authentication and decentralizing identity management. The framework's efficacy is confirmed by experimental results, which show 98% privacy preservation, 700 TPS throughput, 0.98 quantum resilience, and 96% access control effectiveness, making it ideal for contemporary blockchain and IoT applications.
Uzma Shereen, Lubna Nausheen· American Journal of AI Cyber...· 0 citations
A privacy model for searchable symmetric encryption protocols that makes adversarial power a central parameter and induces four privacy levels giving rise to a privacy lattice is proposed, enabling reasoning about how privacy guarantees change under different adversarial capabilities.
Manuela Horduna· Proceedings of the 23rd Inte...· 0 citations
Electronic accounting voucher deposits and cybersecurity audits face a contradiction between resistance to evidence tampering and data privacy protection. Using blockchain technology as a basis, the authors propose a solution framework that addresses both concerns. Under a dual-layer hash mapping architecture, original accounting vouchers are stored off-chain via the InterPlanetary File System, and digital fingerprints are anchored on the immutable ledger. Smart contract engines enforce a multi-signature state machine to ensure accounting voucher traceability from generation to archiving. Paillier homomorphic encryption supports direct computation on ciphertext, enabling continuous consistency checks between accounts and accounting vouchers without decrypting raw data. Experimental results showed that on-chain storage overhead was reduced by 99.08%, audit latency reached 12.4 s under a workload of 5,000 transactions per second, and no raw data were exposed throughout the process. This framework provides forensic-ready support for secure electronic accounting voucher management.
Hui Sui, Ying Sui· International Journal of Dig...· 0 citations