Exploring Use Cases of Fully Homomorphic Encryption in 2025

Written By:
Founder & CTO
June 21, 2025

In a world where digital infrastructure continues to evolve at an unprecedented pace, the demand for robust privacy solutions has become paramount. Developers, especially those building applications in finance, healthcare, artificial intelligence, and data science, are now under immense pressure to ensure data confidentiality without compromising on functionality or performance. This is where Homomorphic Encryption, particularly Fully Homomorphic Encryption (FHE), steps in as a revolutionary advancement.

2025 marks a turning point in the adoption of homomorphic encryption technology, with several real-world use cases transitioning from research to production. In this in-depth blog, we’ll explore what homomorphic encryption is, why developers should care, the specific use cases making headlines in 2025, the technical intricacies, available toolkits, and how to get started today.

What is Fully Homomorphic Encryption (FHE)?

Fully Homomorphic Encryption (FHE) is a cutting-edge cryptographic technique that allows computation on encrypted data, without ever decrypting it. This means you can perform operations like addition, multiplication, and even run machine learning models or statistical computations on data that remains encrypted end-to-end.

In traditional systems, encrypted data needs to be decrypted before it can be processed. This exposes sensitive information during runtime. FHE eliminates this exposure by enabling operations directly on ciphertexts (encrypted values). When the result is eventually decrypted, it matches the output of the same operation had it been performed on unencrypted data.

This unique property of FHE, allowing arbitrary computations on encrypted data, was first proposed theoretically in the 1970s but was long considered computationally impractical. That changed in 2009 when Craig Gentry introduced the first feasible FHE scheme. Since then, significant improvements in cryptographic schemes (like BFV, BGV, and CKKS) and open-source libraries have made FHE a real, usable technology for developers in 2025.

Why Fully Homomorphic Encryption Matters in 2025

The growing concerns over data breaches, compliance regulations, and cloud privacy have placed data-at-use encryption at the center of modern cybersecurity conversations. While encryption-at-rest and encryption-in-transit are now commonplace, encryption-at-use, ensuring data remains encrypted even during computation, was a missing link. That’s exactly what homomorphic encryption provides.

In 2025, with the rise of zero-trust architectures, remote AI inference, and cross-enterprise data sharing, the ability to process sensitive information without decrypting it is not just innovative, it’s essential.

Here’s why developers should be paying attention to FHE in 2025:

  • Regulatory bodies across the globe are mandating privacy-enhancing technologies (PETs) like FHE to comply with laws like GDPR, HIPAA, and CCPA.

  • Cloud providers are offering FHE-as-a-service, making it easier to run secure cloud computation.

  • Hardware accelerators and optimized libraries are reducing the performance gap, making FHE viable for real-world applications.

FHE solves the core challenge: How can we compute insights without compromising privacy? The answer lies in processing encrypted data directly, confidentiality maintained, integrity preserved.

Core Benefits for Developers in 2025
Zero-Trust by Encryption-at-Use

In a zero-trust security architecture, no user or system is inherently trusted, even inside the network. Fully Homomorphic Encryption plays a crucial role here by enabling secure computation without requiring trust in the underlying compute environment. You can send encrypted data to an untrusted server, perform computation on it, and get encrypted results, without ever exposing the raw input.

This model is especially useful for SaaS applications where clients upload sensitive data. Using FHE, developers can process user data while ensuring that not even the backend sees it.

Regulatory Compliance Made Seamless

FHE helps developers design systems that are natively compliant with data privacy regulations. For industries like finance, healthcare, or telecom, where regulatory audits are strict and data residency laws are complex, FHE provides a clean path forward.

Imagine a healthcare analytics app processing patient records. With FHE, encrypted data can be processed across borders without violating data localization laws, because no identifiable information is ever decrypted in the cloud.

Collaborative Analytics Without Raw Data Exposure

Consider two competing banks wishing to collaborate on fraud detection models. Traditionally, they'd be hesitant to share data. With FHE, they can compute on encrypted datasets collaboratively and share results without revealing the underlying sensitive data.

This is called secure multiparty analytics, and in 2025, it’s already being deployed in fintech and healthcare consortia to power encrypted benchmarking, cross-site AI model tuning, and secure statistics sharing.

Private AI & ML Pipelines

Privacy-preserving machine learning is one of the most exciting applications of FHE. Imagine training or running inference on encrypted datasets, where the model never sees the actual data. This unlocks secure healthcare diagnostics, financial risk analysis, and consumer behavior prediction, all without sacrificing user privacy.

Libraries like SEAL-Python, TenSEAL, and HElib now support encrypted vector and matrix operations using schemes like CKKS, enabling developers to embed encrypted AI directly into their pipelines.

Real-World Use Cases of Fully Homomorphic Encryption in 2025

Let’s explore the specific, detailed, and fully implemented use cases of Homomorphic Encryption that developers are actively building and deploying in 2025.

1. Secure Cloud Computing

Cloud computation often involves moving data from secure environments into third-party infrastructure. With FHE, encrypted data can be uploaded, processed, and returned to the client without ever being decrypted on the cloud.

Developers using platforms like AWS or Azure can now integrate FHE libraries into Lambda functions or containerized services to process customer data confidentially. This is a huge leap for industries that rely on cloud-scale infrastructure but require maximum data secrecy.

2. Privacy-Preserving Machine Learning (PPML)

FHE enables training and inference on encrypted data, allowing developers to build end-to-end secure ML pipelines. Developers can now:

  • Run sentiment analysis on encrypted text data.

  • Detect fraudulent transactions on encrypted financial streams.

  • Predict diseases from encrypted patient records.

Frameworks like Concrete-ML, TenSEAL, and HETransformer are equipping ML engineers to deploy models with encrypted inputs, protecting users while maintaining predictive performance.

3. Encrypted Voting and Digital Polling

In secure voting applications, every vote is encrypted before submission. With FHE, these votes can be aggregated and tallied while still encrypted. The final result is decrypted, ensuring end-to-end integrity and voter privacy.

Developers are using FHE to build national-scale e-voting platforms, enabling elections with verified privacy guarantees and no exposure of voter identities or intermediate results.

4. Privacy in Blockchain Smart Contracts

Public blockchains, by design, expose smart contract inputs and outputs. This makes it challenging to build privacy-preserving decentralized applications. With FHE, developers can write smart contracts that accept encrypted inputs, compute over them, and return encrypted results.

Projects like Zama and FHE.org are creating privacy-first blockchain stacks, enabling token swaps, auctions, and voting, all while keeping user data confidential.

5. Biometric Authentication with Confidentiality

Biometrics like face scans or fingerprints are sensitive data points. FHE allows for secure matching of encrypted biometric templates. This means a mobile device can match an encrypted scan against an encrypted database without revealing either party's data.

This breakthrough is already being applied in border security systems and private device unlocking mechanisms.

6. Multi-Party Analytics for Confidential Data

Hospitals, research labs, or government organizations often have overlapping interests in statistical analysis, but data-sharing restrictions block collaboration. Homomorphic Encryption lets these parties encrypt their data and run joint analytics over the combined encrypted dataset.

In 2025, this is being used in:

  • Joint cancer research projects.

  • National fraud detection networks.

  • Economic forecasting by think tanks using sensitive commercial data.

7. Financial Services & Encrypted Wallets

Crypto wallets or financial dashboards are integrating FHE to allow users to perform balance checks, staking rewards estimation, and DeFi analytics on encrypted assets, ensuring full privacy without losing functionality.

Developer-Centric Technical Insights
Choosing the Right Scheme: BFV, CKKS, BGV
  • BFV: Best for exact integer operations (e.g., census data, ID analytics).

  • CKKS: Ideal for real-number approximate computation (e.g., ML pipelines).

  • BGV: Good performance and flexibility, especially for logic-based computation.

Each scheme comes with tradeoffs in terms of accuracy, performance, and complexity. Choose based on whether you're doing secure AI, encrypted stats, or cloud analytics.

Libraries and SDKs in 2025
  • Microsoft SEAL: Widely used, well-documented, ideal for C++ and Python developers.

  • HElib: Focused on research but powerful for advanced implementations.

  • PALISADE: Balanced performance and scalability.

  • OpenFHE: Modular, with industry backing.

  • Concrete-ML: ML developers’ go-to toolkit for encrypted model deployment.

Key Handling, Bootstrapping, and Performance

FHE involves working with large keys and ciphertexts. Developers need to handle:

  • Noise management: Bootstrapping resets noise levels, allowing deeper computations.

  • Ciphertext packing: Batching multiple values into one ciphertext.

  • Parallelization: GPU support and SIMD techniques are becoming standard in 2025.

Challenges Developers Must Address

Even though the field has advanced significantly, developers still face challenges like:

  • Compute Overhead: FHE computations are still 10³–10⁶× slower than plaintext ones.

  • Memory Usage: Ciphertexts can be hundreds of kilobytes.

  • Complex Tooling: Many FHE libraries have steep learning curves.

  • Circuit Design: Building efficient computation graphs is critical for real-world latency.

However, with modern compilers and abstraction libraries, most of these challenges are surmountable.

Developer Advantages Over Traditional Encryption
  • Traditional encryption requires data decryption before use. FHE keeps data encrypted throughout.

  • Secure enclaves (TEEs) require hardware trust. FHE is hardware-independent and mathematically secure.

  • Multi-party computation (MPC) requires coordination. FHE allows independent secure processing.

FHE offers mathematically pure, end-to-end encrypted computation, giving developers full control over privacy, without additional hardware dependencies.

Getting Started With FHE as a Developer

To begin building with Fully Homomorphic Encryption:

  1. Pick a narrow use case, like calculating a sum over encrypted survey results.

  2. Choose a library, SEAL-Python is beginner-friendly.

  3. Prototype computations, start with simple arithmetic or logic.

  4. Test performance, measure latency, ciphertext size, noise growth.

  5. Gradually integrate, build FHE-powered modules into larger applications.

Once you’re confident, move towards secure AI, encrypted APIs, or even blockchain privacy layers.

Looking Ahead: 2025 to 2030

The future of homomorphic encryption is promising. Between 2025 and 2030, we expect:

  • Hardware-accelerated FHE chips for real-time encrypted computation.

  • Cross-language FHE SDKs for Node.js, Go, Rust.

  • Standardized FHE APIs governed by consortiums like FHE.org and HESC.

  • Mainstream adoption in AI platforms, data clouds, and enterprise SaaS.