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Öğe EVALUATION OF G7 COUNTRIES IN TERMS OF THEIR DIGITAL INTELLIGENCE PERFORMANCE WITH THE CoCoSo METHOD(IGI GLOBAL, 2025) Eren, Hande; Gelmez, ErenDigital technologies are increasing their importance on a global scale day by day. New concepts have emerged as the effects of digital technologies are felt on both individuals and businesses. One of these concepts is digital intelligence. In this context, the main purpose of this study is to examine the digital intelligence performances of G7 countries. Within the framework of this main purpose, the digital intelligence performances of G7 countries were determined by using the Digital Intelligence Index (2020). The current index includes six criteria: state, momentum, environment, experience, behavior and attitudes. Within the scope of the analysis, the SD (Standard Deviation) method was used in weighting the criteria and the CoCoSo (Combined Compromise Solution) method was used in ranking the alternatives/countries. According to the SD method results, it was seen that the criterion with the highest importance level was behavior, while state had the lowest importance level. After weighting the criteria, CoCoSo analysis was conducted and as a result of the analysis, it was determined that the country with the highest digital intelligence performance was the USA; Germany followed the USA. The countries with the lowest performance were determined as France and Italy, respectively.Öğe AI Model Validation with ZeroKnowledge Proof: Trust and Transparency Without Data Access(Duvar Yayınları, 2025) Taş, ÖzgeSummary Zero-Knowledge Proof Machine Learning (ZKML) is a new approach that combines zero-knowledge proofs (ZKP) with machine learning (ML) to develop privacy-focused and secure artificial intelligence systems. ZKP are cryptographic techniques that enable one party to prove the validity of certain information without disclosing any additional data. This mechanism is particularly important in fields that require high levels of privacy, such as finance, healthcare, and identity verification. ZKML enables the cryptographic verification of the accuracy of model inferences or training processes without disclosing model parameters or user data. In the context of federated learning, the accuracy of each participant's contribution to the model training process can be verified using proof systems such as zk-SNARKs, thereby enabling a secure collaboration environment without the risk of data leakage. Similarly, during the inference phase, it can be verified whether the model produced a specific output, which builds trust in fields such as medicine and finance where sensitive decisions are made. Currently, the production of ZK proofs requires high computing power. However, thanks to advances in hardware, distributed systems, and cryptography, proof production is now more feasible even for larger and more complex models. Startups like Modulus Labs and tools like the ezkl library enable the production of ZK proofs on models in ONNX format, offering practical solutions to developers. Systems like Plonky2 have reduced proof production for models with millions of parameters to just minutes. ZKML has a bunch of use cases, including on-chain ML verification (e.g., in DeFi protocols), transparency of ML services (MLaaS), fraud detection, and private inference. For example, in decentralized Kaggle-like systems, the accuracy of a model can be proven without revealing its details. In healthcare, patients can access diagnostic results without disclosing their data. In conclusion, ZKML combines privacy protection with the security of verification processes, enabling the development of more ethical and reliable artificial intelligence systems. This approach, which lies at the intersection of cryptography and machine learning disciplines, has the potential to increase the transparency and security of AI systems at both technical and societal levels.
