KP
K. Penchev
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Trust in network nodes
Implementation of secure communication and data storage using a post-quantum encryption algorithm and signature scheme with a blockchain environment
The development of quantum computing poses a significant threat to currently deployed cryptographic primitives. To address this challenge, we design and implement a system to communicate and store data using post-quantum secure algorithms for a distributed blockchain environment. Specifically, we combine the Crystal-Dilithium digital signature scheme and an Updatable Encryption scheme based on the Learning With Errors problem in the context of a Hyperledger Fabric network. The Crystal-Dilithium scheme ensures authenticity and integrity of transmitted data, while the Updatable Encryption scheme provides confidentiality and integrity to transmitted data as well as allowing key rotation and ciphertext updates without requiring data re-encryption from scratch. An end-to-end prototype of the system is developed, including client-side modules, a Hyperledger Fabric gateway and smart contract modules that can be deployed on a Hyperledger Fabric network, with all components implemented in Python and Golang. The system is evaluated through functional integration tests and performance benchmarks. Results demonstrate that the Crystal-Dilithium scheme achieve sub-second execution for key generation, signing, and verification for multiple different parameter sets. For the Updatable Encryption scheme, encryption, decryption, and ciphertext updates exhibit linear scalability in time complexity with respect to the size and dimensions of input parameters, while token generation emerges as the most computationally demanding step in the algorithm. Additional experiments highlight the practical limits of repeated key rotations due to accumulated error growth.
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The development of quantum computing poses a significant threat to currently deployed cryptographic primitives. To address this challenge, we design and implement a system to communicate and store data using post-quantum secure algorithms for a distributed blockchain environment. Specifically, we combine the Crystal-Dilithium digital signature scheme and an Updatable Encryption scheme based on the Learning With Errors problem in the context of a Hyperledger Fabric network. The Crystal-Dilithium scheme ensures authenticity and integrity of transmitted data, while the Updatable Encryption scheme provides confidentiality and integrity to transmitted data as well as allowing key rotation and ciphertext updates without requiring data re-encryption from scratch. An end-to-end prototype of the system is developed, including client-side modules, a Hyperledger Fabric gateway and smart contract modules that can be deployed on a Hyperledger Fabric network, with all components implemented in Python and Golang. The system is evaluated through functional integration tests and performance benchmarks. Results demonstrate that the Crystal-Dilithium scheme achieve sub-second execution for key generation, signing, and verification for multiple different parameter sets. For the Updatable Encryption scheme, encryption, decryption, and ciphertext updates exhibit linear scalability in time complexity with respect to the size and dimensions of input parameters, while token generation emerges as the most computationally demanding step in the algorithm. Additional experiments highlight the practical limits of repeated key rotations due to accumulated error growth.
Negotiation with the help of a negotiation agent
How the agent’s negotiation style affects trust
Negotiation is a challenging process for people, which often results in suboptimal agreements between the negotiating parties. This issue leads to lost benefits that one of the negotiating parties could have obtained. To counteract this drawback artificially intelligent negotiation agents are developed. Their goal is to help negotiating parties increase the overall benefits from a negotiation. However, the main drawback of such agents is that people need to trust the agents in order to use them. In this research paper we investigate whether a negotiating agent with a hardliner negotiating style would be trusted more than a negotiating agent with a conceder negotiation style. We answer this question by conducting an user study, where we measure participants' trust in the two negotiation agents and compare the results. From the results we concluded that the assumption is correct for the sample group that participated in the user study.
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Negotiation is a challenging process for people, which often results in suboptimal agreements between the negotiating parties. This issue leads to lost benefits that one of the negotiating parties could have obtained. To counteract this drawback artificially intelligent negotiation agents are developed. Their goal is to help negotiating parties increase the overall benefits from a negotiation. However, the main drawback of such agents is that people need to trust the agents in order to use them. In this research paper we investigate whether a negotiating agent with a hardliner negotiating style would be trusted more than a negotiating agent with a conceder negotiation style. We answer this question by conducting an user study, where we measure participants' trust in the two negotiation agents and compare the results. From the results we concluded that the assumption is correct for the sample group that participated in the user study.