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Honored to be Nominated for The Blockchain 100! I’m thrilled to share that I’ve been nominated in the Top 100 Independent Researcher category for the Binance Blockchain 100 Awards. This recognition celebrates those driving innovation and contributing original research to the blockchain ecosystem. ✅Community Voting is now open! Your support means a lot—please cast your vote here: 👉Vote Now: Binance Let’s continue building and shaping the future of blockchain together! #Blockchain100 #Binance #BlockchainInsights #CryptoResearch #CommunityPower

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Cryptosoftware.ist: Turkey's Leading Cryptocurrency Software Company

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Data privacy and Decentralized Artificial Intelligence (DeAI). PART 2 As an example, in online social networks (OSNs) use of the decentralized structures is more concerned with user privacy than the centralized structures. The talks at conference such as KBW 2025 and Token2049 reference the importance of DeAI in enhancing the safety of data; research initiatives such as GAIA focus on zero-knowledge proofs (ZK proofs) used to verify ownership of data. Difficulties remain, though: distributed systems can be expensive to coordinate and scalability is a problem. These can be solved by future integrations with Confidential Computing. DeAI has an ethical aspect on an academic level; it minimizes bias in data and enhances equity. The studies highlight the importance of blockchain in improving security and transparency in DeAI. Finally, standardization and regulations are necessary in order to have DeAI as a promise of a privacy-focused AI future. $GAIA

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Data privacy and Decentralized Artificial Intelligence (DeAI). PART 1 The concept of decentralized artificial intelligence (DeAI) is a new paradigm that resolves problems related to data privacy of centralized AI systems. This paper discusses the data protection systems, blockchain integrations, and possible uses by DeAI. Conventional AI solutions are based on large volumes of data stored in centralized servers, which puts the privacy of users at risk of data breaches. DeAI, however, is based on distributed technologies such as blockchain to restore ownership of data to the user, improving transparency and security.Federated learning and homomorphic encryption are the approaches underlying the principles of DeAI. Federated learning allows local processing of data on computers, with model updates only being sent to a central server, and raw data is not sent. Such method is especially useful in healthcare where AI models can be trained with using patient data without any violations of privacy. An example is DeCaPH framework, which supports collaborative learning within a multi-hospital environment, which guarantees privacy-sensitive partnerships. By using blockchain as a part of DeAI, smart contracts can control access to data; when the user consents to share data, the operation is registered as permanent. This builds transparency in data markets and avoids privacy threats.In practice, DeAI privacy-conscious AI agents act as blockchain custodians. Such guardians are a combination of lightweight AI models and blockchain protecting the information of users. $GAIA

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