Fully Homomorphic Encryption (FHE) is such a new concept that its ISO standard is still under development. But FHE is a real thing (nobody would work on an ISO standard for something that does not have real utility), and there is a lot of hype around it, and for a good reason. Let’s discover it together and look at 5 examples of how you can build your own FHE-powered product.
Traditional encryption encrypts data end-to-end, but not at the endpoints themselves. Data must still be decrypted to be processed. For instance, when a banking system updates a customer’s balance, it must know the old balance, transaction amount, and new balance.
FHE keeps data encrypted throughout its entire lifecycle. This fundamentally changes data handling, significantly reducing vulnerabilities. Most data leaks occur during processing when data is decrypted and exposed.
With FHE, even banking software never sees your actual balance. It processes encrypted inputs and generates encrypted outputs, viewable only by the account owner and compliance officers.
Real-world examples of utilizing Fully Homomorphic Encryption (FHE):
1. Tokenization of assets
HSBC and Citibank use FHE for asset tokenization.
Problem: Most deal registrations currently occur off-chain because customers highly value privacy, not just registration of rights. On-chain registration offers benefits:
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Enhanced cross-usability of tokens with complex on-chain DeFi instruments.
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Greater trust due to blockchain transparency compared to off-chain registrations.
But due to blockchain transparency, big players prefer to stay off-chain and protect the knowledge of their holdings away from the public
Solution: FHE enables confidential transactions compliant with regulations. Transactions remain private yet auditable by compliance authorities. Anyone can validate a transaction’s correctness, but only the transaction owner and authorities can see its details and know who owns what.
2. Decentralized Autonomous Organizations (DAOs)
Problem: Unencrypted DAOs reveal excessive information about shareholders. Even minor transaction details can disclose significant insights into shareholders’ rights and financial status. Consequently, most organizational activities remain off-chain to protect shareholder privacy.
Solution: FHE enables private yet fully compliant execution of DAO activities. Wallets submit encrypted votes and inputs that remain encrypted while still verifiable for correctness by network participants. Only wallet holders and authorized individuals can view transaction details, maintaining an audit trail with logged data access.
3. Encrypted Stablecoins
Problem: Stablecoins are increasingly integrated into daily life, legally accepted in local stores across many countries. However, their transparency is problematic—once someone pays us, we can track their wallet transactions and balances. In certain regions, this transparency has led to criminal activities like kidnappings for ransom.
Solution: An encrypted, compliant stablecoin would allow wallet owners and authorities to see balances and transactions, but prevent others from accessing this information.
4. FHE ASICs
Problem: FHE’s current barrier is its computational complexity. Presently, FHE effectively encrypts only very small data sets (like NFTs or blockchain transactions). Processing larger data (images, videos, texts, or voice recordings) is currently infeasible due to performance constraints.
Solution: There are two main strategies to address this challenge:
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Optimizing FHE algorithms and solutions.
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Developing hardware specifically designed for FHE acceleration.
ASICs (Application-Specific Integrated Circuits) are specialized processors with limited instructions tailored to FHE tasks, cheaper to produce, and highly power-efficient.
5. AI-enabled Therapy Platform
Problem: Large Language Models (LLMs) like ChatGPT and Grok are widely used for everyday tasks, and therapists frequently consult them to enhance patient care. However, this practice typically violates HIPAA regulations due to confidentiality concerns. Even though insurance companies cannot catch individual therapists sending their conversation history with a patient to ChatGPT window, it is still illegal. Any legal solution would be welcomed by many market players.
Solution: Therapists could encrypt patient health records with FHE, processing them securely using FHE-compatible LLMs. This approach provides accurate results without exposing sensitive patient data, ensuring full HIPAA compliance. An early market entrant with this capability would likely dominate the space.
Note: For organizations interested in entering this market, we offer a white-label online therapy platform ready for transformation into an FHE-powered, HIPAA-compliant service. This platform would support therapist marketplaces, therapy sessions, and LLM integrations, uniquely positioning it for rapid market adoption. Many FHE features can be taken readymade from tool providers like ZAMA
