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Unchained

How Zcash and NEAR Are Driving This Crypto Bull Run

Wednesday, 23 September 2026 · 2 min read · Listen to the episode ↗

In this episode, the hosts explore how Zcash and NEAR are driving the current crypto bull run, with Zcash experiencing a 32% increase and NEAR surging 69%. They discuss Zcash's unique privacy features, including zero-knowledge proofs, and its integration with NEAR's multi-party computation for enhanced decentralized finance applications. The episode also highlights upcoming upgrades to Zcash that aim to improve security and scalability, while addressing the growing demand for privacy in blockchain transactions.

Zcash and NEAR are pivotal in the current crypto bull run, with Zcash seeing a 32% increase and ranking ninth on CoinGecko, while NEAR has surged 69% to rank 23rd. Ilya Polisukhin emphasizes the potential of private confession computers built with private shards for financial applications, addressing the critical issue of on-chain privacy necessary for everyday blockchain use.

Mert Mumtaz points out that Zcash's encryption and quantum security make it appealing to cryptocurrency users, but he believes its price performance is influenced by broader narratives beyond its privacy features. He warns that as more chains adopt privacy capabilities, Zcash's unique position may weaken. Mumtaz also highlights the importance of privacy for a store of value, which Zcash provides, especially when paired with NEAR for decentralized finance (DeFi) applications.

Elia discusses the increasing demand for privacy in blockchain transactions, asserting that Zcash's features promote participation in the global economy. He predicts that privacy will be a key driver in the next cryptocurrency adoption cycle. The episode details how Zcash's zero-knowledge proofs ensure user balance confidentiality, while NEAR aims to offer fully programmable privacy through technologies like multi-party computation and secure enclaves.

Elliot notes that NEAR's multi-party computation network enables seamless transactions across chains, with a total value locked (TVL) of $206 million, including $71 million from Zcash. The integration of Zcash into NEAR's ecosystem allows users to engage in various financial activities while maintaining confidentiality, catering to both consumer and institutional privacy concerns in DeFi.

Zcash faces a vulnerability that could have allowed counterfeit coin minting, though it remains unclear if this was exploited. This issue was limited to the orchard pool, which allowed new coin creation. A proposed turnstile system aims to prevent undocumented minting, and the migration from orchard to ironwood has been verified three times. The new proof in Zcash indicates that undetectable counterfeit bugs cannot exist, with over 90% of funds having migrated to ironwood.

Zcash's anonymity complicates post-issue detection, while NEAR's multi-party computation system decentralizes transaction signing and uses a verifier contract to track balances and transactions across assets. NEAR's decentralized system includes high-quality operators, such as financial institutions, and the cost of verification modification is significantly lower than traditional systems. The episode underscores the importance of security in crypto, with available tools to mitigate risks.

Zcash is preparing for another upgrade, which may be contributing to its price increase. A significant upgrade will reduce block times from 75 seconds to 25 seconds, enhancing interoperability with NEAR and boosting confidence in transaction correctness. Project Hack, part of the Zcash upgrade, aims to scale Zcash by a hundred times and ensure full quantum proofing. The new shielded pool will feature simpler ZK circuits, expected to reduce risk and enhance security. Zcash is also exploring governance improvements, including private voting for coin holders, with these developments anticipated to enhance security, scalability, and quantum proofing. However, the numerous updates in the Zcash release make it challenging to track all changes.

This summary was generated from the episode transcript and can contain mistakes.