As digital transformation accelerates, organisations and individuals are increasingly sharing, processing, and storing data in cloud environments. However, privacy concerns remain paramount, particularly for sensitive data such as health records, financial transactions, and proprietary business insights. Traditional encryption ensures data confidentiality at rest and in transit but requires decryption for computation, exposing data to risks during processing.

Enter homomorphic encryption (HE) – a breakthrough cryptographic technology that allows computations to be performed directly on encrypted data without decryption. This ensures data remains protected even during processing, enabling true privacy-preserving computation.

In this blog, we will explore what homomorphic encryption is, how it works, real-world use cases, and how public and organisations can leverage emerging HE tools to enhance data privacy and regulatory compliance.

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Understanding Homomorphic Encryption

Homomorphic encryption is an encryption method that allows mathematical operations to be performed on ciphertexts, producing encrypted results which, when decrypted, match the results of operations performed on the plaintexts.

In simple terms:

• Traditional encryption: Data must be decrypted for processing, risking exposure.

• Homomorphic encryption: Data remains encrypted throughout, and computations on ciphertext yield valid results post-decryption.

For example:

• Encrypt(2) = Enc(2)

• Encrypt(3) = Enc(3)

• Compute Enc(2) + Enc(3) = Enc(5)

• Decrypt(Enc(5)) = 5

This groundbreaking property enables data to remain private even when processed by untrusted parties or outsourced services such as cloud providers.

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Types of Homomorphic Encryption

1. Partially Homomorphic Encryption (PHE): Supports only one type of operation (either addition or multiplication). Example: RSA (multiplicative), Paillier (additive).

2. Somewhat Homomorphic Encryption (SHE): Supports limited operations and depth.

3. Fully Homomorphic Encryption (FHE): Supports arbitrary computations on ciphertexts, enabling any combination of operations without decrypting.

The concept of Fully Homomorphic Encryption was first realised in 2009 by Craig Gentry. While earlier FHE implementations were computationally intensive, modern research and tools are making it increasingly practical for selective use cases.

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Why Is Homomorphic Encryption Important?

1. Privacy-Preserving Data Processing: Enables third parties (e.g. cloud services, analytics providers) to process sensitive data without accessing the underlying plaintext.

2. Regulatory Compliance: Aligns with data protection regulations such as GDPR, HIPAA, and India’s DPDP Act, which emphasise data confidentiality and minimising exposure.

3. Secure Outsourced Computation: Organisations can offload computation-heavy tasks to the cloud without revealing proprietary data or personal information.

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How Do Homomorphic Encryption Tools Work?

HE tools implement cryptographic libraries and APIs that:

✅ Encrypt data using homomorphic schemes (e.g. BGV, BFV, CKKS).
✅ Allow computations (addition, multiplication, polynomials, statistical analysis) directly on ciphertexts.
✅ Decrypt the final output to reveal results without intermediate exposure.

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Leading Homomorphic Encryption Tools and Libraries

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Real-World Use Cases of Homomorphic Encryption

1. Privacy-Preserving Medical Analytics

Hospitals and research institutions hold sensitive patient data crucial for epidemiological studies and AI model training. Sharing decrypted data risks violating HIPAA or GDPR.

Example:
A pharmaceutical company wants to analyse patient data from multiple hospitals to discover treatment efficacy trends. Using homomorphic encryption:

• Hospitals encrypt their data using a shared HE scheme.

• The pharmaceutical company performs aggregate analysis on encrypted data.

• Decrypted results reveal only the statistical outcomes, not individual patient records.

This ensures compliance while advancing medical research.

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2. Secure Financial Computations

Banks and fintech companies often require third-party risk assessments, credit scoring, or fraud detection services. Sharing raw transaction data with vendors exposes sensitive financial information.

With HE:

• Transaction data is encrypted using an FHE scheme.

• The risk assessment vendor runs fraud detection algorithms on ciphertexts.

• Results are decrypted by the bank to obtain insights without revealing customer data to vendors.

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3. Privacy-Preserving Machine Learning (PPML)

AI models trained on sensitive data risk exposing underlying inputs via inference attacks. HE enables encrypted model training or encrypted inference, enhancing data confidentiality in AI workflows.

Example:
A cloud-based AI service offers disease prediction models. Patients’ hospitals encrypt medical inputs, send them to the cloud service for prediction, and receive encrypted outputs, ensuring patient data is never visible to the AI service provider.

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Example for Public Users

While fully homomorphic encryption is computationally intensive and mainly used in institutional contexts today, public users benefit from applications integrating HE for privacy.

For instance:

• Encrypted Health Apps: Some emerging telemedicine apps use HE-backed APIs to analyse health metrics without exposing raw data to app vendors.

• Secure Password Managers: Future password managers may leverage HE to check password breach status without revealing the actual password to breach-checking services.

Public users should look for privacy-focused apps that adopt HE or similar privacy-enhancing technologies (PETs) for enhanced data confidentiality.

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Limitations and Challenges

Despite its promise, homomorphic encryption has limitations:

🔴 Performance Overheads: FHE operations are thousands of times slower than plaintext computations, making real-time processing challenging.

🔴 Complex Implementation: HE requires specialised cryptographic expertise, careful parameter selection, and secure key management.

🔴 Limited Support for Some Operations: While HE supports addition and multiplication efficiently, certain operations (e.g. division, comparisons) remain computationally intensive.

However, ongoing research is addressing these challenges, and practical deployments in specific domains are becoming viable.

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The Future of Homomorphic Encryption

As performance improves and libraries mature, HE will become an essential privacy-enhancing technology powering:

• Secure cloud analytics services for healthcare, finance, and government sectors

• Federated learning with HE-based encrypted model aggregation

• Cross-organisation data collaborations without data sharing risks

• Encrypted biometric authentication systems enabling matching without exposing templates

Leading cloud providers like Microsoft, IBM, and Google are actively researching and integrating HE to offer privacy-preserving computation as a service (HEaaS) in the near future.

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Best Practices for Organisations Adopting HE

1. Identify High-Risk Data Workloads: Focus HE deployment on workloads involving sensitive data processing by external parties.

2. Use Established Libraries: Adopt mature libraries like Microsoft SEAL or IBM HElib with community and vendor support.

3. Combine with Other PETs: Integrate HE with differential privacy, secure multi-party computation (SMPC), or trusted execution environments (TEE) for layered privacy.

4. Evaluate Performance Impacts: Conduct performance assessments to ensure feasibility within operational constraints.

5. Train Security Teams: Ensure cryptographic and development teams are trained on HE schemes and implementation considerations.

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Conclusion

Homomorphic encryption tools represent a paradigm shift in data security, enabling computations on encrypted data while maintaining confidentiality throughout processing. In a world where data privacy is non-negotiable, HE offers a pathway to leverage cloud computing, AI, and data collaborations without compromising sensitive information.

Key Takeaways:

✔️ Homomorphic encryption allows computations on ciphertexts, preserving data privacy during processing.
✔️ Use cases include privacy-preserving analytics, secure financial computations, and AI model training.
✔️ Tools like Microsoft SEAL, IBM HElib, and PALISADE are advancing HE adoption.
✔️ While computationally intensive, HE is becoming practical for selective high-risk workloads.
✔️ HE empowers organisations to comply with data protection regulations while extracting value from encrypted data.

As organisations prioritise privacy by design, integrating homomorphic encryption into their data processing pipelines will become an essential competitive and compliance advantage in the years ahead.