Privacy-Preserving Blockchain Models Using Zero-Knowledge Proofs

Abstract

Public blockchains are radically transparent -- every transaction, every balance, every smart contract interaction is visible to anyone with an internet connection. That transparency is a feature for auditability but a catastrophe for privacy. When Chainalysis or Arkham can link a wallet address to a real identity, the entire financial history of that person becomes public. Zero-knowledge proofs offer a way out: they let a prover convince a verifier that a statement is true without revealing anything beyond the truth of the statement itself. But the ZK landscape is fragmented -- Groth16, PLONK, STARKs, Halo2, Nova -- and each proof system makes different trade-offs in proof size, verification time, prover cost, and trust assumptions. We present the Zero-Knowledge Privacy Assessment Framework (ZKPAF), evaluating six ZK-based privacy models -- shielded transactions (Zcash-style), private smart contracts (Aleo/Aztec), ZK-rollups with privacy, confidential tokens, ZK credential proving, and private voting -- across four proof systems (Groth16, PLONK, STARKs, Halo2) on real application workloads. Our Privacy Model Quality Score (PMQS) measures privacy guarantee strength, proof generation time, verification cost, GDPR compatibility, and user experience. Private smart contracts on Aztec achieve the highest PMQS (0.916) by providing programmable privacy with composable private state, while ZK credential proving achieves the strongest GDPR compatibility (0.960).