MindChats EP05 Recap with VCs on Fully Homomorphic Encryption
In the 5th episode of MindChats, streamed on March 7th, the Mind Network delved into the transformative landscape of Fully Homomorphic Encryption (FHE) and the market in general. A stellar lineup of VC specialist speakers, including Kyle Chassé, Mads Pedersen were invited. With 9.1K tuned-in participants, the conversation explored the potential applications of FHE, its challenges, and its strategic placement in the investment theses of venture capitalists.
Time Stamp
Check out the recording of the AMA：
https://twitter.com/i/spaces/1ypKdkAyyRdxW?s=2005:54 Introduction of VCs
11:06 FHE and shared state exploration
22:15 FHE applications in Web3
27:48 ZK and FHE Difference
30:31 DID connection
31:52 Data assets and digital assets
35:10 Talks on Pocket Network (DePIN)
39:08 Steps to make FHE accessible for retail
51:39 Privacy-preserving, privacy infrastructure in investment theses
55:39 Shoutout to the FHE article
58:01 Closer
Key Highlights of the Episode
FHE and Shared State Insights
- Fully Homomorphic Encryption (FHE) removes the decryption requirement across validators, ensuring complete confidentiality in multiplayer scenarios through a shared state.
- Applications range from private lending to secure multiplayer strategy games.
- FHE’s encrypted shared state prevents users from being front-run by others in the game and from having their transaction details seen by unauthorized parties.
- It prevents others from imposing malicious Minimum Viable Validators (MVV) on the user.
Challenges with FHE
- The primary challenge lies in the speed of FHE, with CPUs managing only 2 to 3 transactions per second.
- GPUs offer a modest improvement, reaching around 10 to 15 transactions per second.
Future Applications of FHE
- FHE networks thrive in low-liquidity environments.
- Applications include private voting, secure storage of DIDs, and encrypted credit scores.
- It can be used in gaming, where certain transactional components are encrypted.
- FHE allows for secure computation without prior decryption.
Differentiator between FHE and ZK
- FHE enables computations over encrypted data without prior decryption.
- ZK focuses on proving data validity, but the prover gains access to all user data, which compromises privacy.
- FHE’s confidentiality can be combined with ZK to verify data integrity and with MPC to distribute data across chains.
FHE Accessibility and Appeal
- Ongoing projects like Zama, Elusiv, Fhenix, Inco, and Fair Math aim to make FHE more accessible.
- Tools and frameworks are being developed to integrate FHE into various blockchain layers.
- This enables FHE to become programmatically confidential and adaptable to different use cases.
FHE in Investment Theses
- Programmable confidentiality is acknowledged as a feature, not the entire product.
- Branding solely as a privacy-preserving blockchain can be challenging while penetrating the core user space.
- Privacy features complement the broader product in investment theses.
- Middleware solutions between L1 and L2 can bring better partnership opportunities.
About Mind Network
Mind Network is the first FHE Restaking Layer for POS and AI Networks. Our framework operates as an FHE validation network, bringing secure computation and consensus to AI, DePIN, EigenLayer AVS, Bittensor Subnet, and many critical POS networks. Mind Network’s solution has found initial product-market-fit with projects such as IO.Net, MyShell, Bittensor, AIOZ, Nimble AI, ChainLink, Connext and more adopting its solution. It has achieved 650k+ active users and 3.2M+ transactions processed on its testnet.
Mind Network is backed by Binance Labs, Hashkey, Big Brain, Chainlink, Comma3, SevenX, and received an Ethereum grant to conduct research on practical implementations for FHE technology on Ethereum.
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