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Development of a Blockchain-Based Lottery Distributed Application Using Blake3 Cryptographic Hashing Function

Abstract

This study addresses long-standing issues of fairness, transparency, and efficiency in traditional lottery systems. Centralized models often suffer from manipulation risks and lack of public verifiability. To solve this, a decentralized lottery DApp was developed using Solana blockchain and the BLAKE3 cryptographic hash function. The goal was to improve randomness, cost-efficiency, and user trust in lottery operations. The system followed the Waterfall Model for structured development. Rust and React.js were used for backend and frontend development respectively. Phantom Wallet was integrated for secure identity and transaction handling. BLAKE3 served as a secure and fast RNG for transparent lottery draws. Key functionalities, including ticket purchase and prize distribution, were managed by smart contracts. Testing on Solana Playground showed low latency and high throughput. Transaction speeds exceeded 3 TPS, with deployment costs between $1.08 and $1.95 per contract. Compared to Ethereum, Solana offered significant cost advantages. User testing showed smooth interactions and secure lottery operations. The system proved scalable, transparent, and cryptographically secure. This research validates the use of BLAKE3 and Solana for fair lottery systems. It offers a practical benchmark for developers and researchers building blockchain-based games or financial tools. Keywords: Solana, BLAKE3, Decentralized Lottery, Blockchain, Transparency Saka, A.O., Olabiyisi, S.O., Alawode, A.O., Oyeleye, C.A.., Oguntola, K.M. & Ogunleye, T.O. (2025): Development of a Blockchain-Based Lottery Distributed Application Using Blake3 Cryptographic Hashing Function. Journal of Advances in Mathematical & Computational Science. Vol. 13, No. 2. Pp 80-93. Available online at www.isteams.net/mathematics-computationaljournal. dx.doi.org/10.22624/AIMS/MATHS/V13N2P7

Research topics

  • FinTech, Crowdfunding, Digital Finance
  • Technology Adoption and User Behaviour
  • Recommender Systems and Techniques

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DOI: 10.22624/aims/maths/v13n2p7

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