A Survey on Zero-Knowledge Proofs: Trade-Offs and Application-Oriented Adaptation

Abstract

The increasing demand for verifiable computation in privacy-sensitive distributed systems has driven the widespread adoption of Zero-Knowledge Proofs (ZKPs). However, the various kinds of current ZKP frameworks—which include zk-SNARKs, zk-STARKs, Bulletproofs, and folding-based systems—introduce complex trade-offs across proof size, prover cost, and trust assumptions, making system selection challenging in actual practice. This paper presents a systematic, application-oriented survey that connects ZKP design choices with real-world deployment constraints. It provides a comparative analysis of major constructions to evaluate their performance and security properties. Furthermore, these trade-offs are mapped to representative application scenarios, including Layer 1/Layer 2 blockchain scaling, Decentralized Identity (DID), and Verifiable Machine Learning (zkML), explaining how different systems are selected based on application-specific requirements. In addition, the paper discusses emerging paradigms such as hardware acceleration, binary field optimizations, and lookup-based zkVMs, which aim to address the prover bottleneck. Overall, this survey provides a structured understanding of the strengths and limitations of existing ZKP systems and offers insights for the design of scalable and privacy-preserving infrastructures.