Homomorphic Encryption Market Projected to Reach USD 346.04 Million by 2032, Driven by the High Demand for Data Privacy & Security in Sensitive Industry | SNS Insider

The global homomorphic encryption market is being driven by the need for data privacy and security in sensitive business sectors including government, healthcare, and BFSI. Growing adoption of homomorphic encryption in BFSI and healthcare for secure data computation.

Austin, Sept. 29, 2025 (GLOBE NEWSWIRE) — The Homomorphic Encryption Market Size was valued at USD 199.25 Million in 2024 and is expected to reach USD 346.04 Million by 2032 and grow at a CAGR of 7.16% over 2025-2032.

The primary factor propelling the global homomorphic encryption market is the rising need for safe data processing in highly regulated industries like government, healthcare, and BFSI. These sectors deal with extremely sensitive data, including patient information, financial transactions, and defense data that must adhere to strict laws like the California Civil Code, the GDPR in Europe, and HIPAA in North America. Conventional encryption techniques can secure data while it is being stored or transported, but they still require decryption during processing, which introduces vulnerabilities. Compute over encrypted data without disclosing it at all thanks to homomorphic encryption, which completely eliminates that risk.  

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Key Players:

• IBM
• Microsoft
• Google
• Intel
• Thales
• NEC
• Zama
• Duality Technologies
• Enveil
• Inpher
• Cosmian
• CryptoExperts
• Galois
• Huawei Cloud
• ShieldIO
• Cornami
• DESILO
• CryptoLab
• Oracle
• Optalysys

Homomorphic Encryption Market Report Scope:

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Segmentation Analysis:

By Component, in 2024, Solution Led the Market with 65.20% Share in 2024, while Services are the fastest-growing segment with a CAGR 7.32%

In, 2024, the market was dominated by the solution segment as enterprises are continuing to prioritize the hardware-based custom solutions for ensuring secure data computation in the cloud technology. The fastest growing segment of the market is Services on account of the high demand for consulting, integration, and managed services that help organizations in the effective implementation of complex homomorphic encryption systems into their organizational environments.

By Method, in 2024, Full Encryption Led the Market 48.08%, while Somewhat Encryption is Expected to be the Fastest-growing Segment with a CAGR 7.63%

In 2024, the Full Encryption segment is the largest type in the Homomorphic Encryption Market as organizations dealing with sensitive data across BFSI, healthcare and government verticals prefers complete envelop of maximum security. Somewhat Encryption segment is the fastest-growing, which is a hybrid between fully and partially homomorphic encryption, offering some level of encryption and faster processing with lower computational overhead, making it a perfect fit for real-time applications and AI/ML workloads.

By Application, in 2024, Secure Data Computation Held the Dominant Market Share of 40.08%, while Data Monetization is the Fastest-growing Segment with a CAGR 9.96%

The Secure Data Computation segment dominated the market in 2024 due to the increasing need among organizations to perform analytics and operations on enquiries with sensitive datasets without exposing raw information. The Data Monetization segment is the fastest-growing one with the burgeoning need of enterprises to confidently share or sell encrypted datasets to partners or third parties

By End Use, in 2024, BFSI Led the Market with Share of 30.14%, while Healthcare is the Fastest-growing Segment with a CAGR of 8.63%

The Banking, Financial Services, and Insurance (BFSI) segment dominated the Homomorphic Encryption Market in 2024, as banks, insurance companies, and financial institutions work to securely compute sensitive data for fraud detection, risk assessment and regulatory compliance. Healthcare segment is the fastest growing segment, attributed to the adoption of privacy-preserving encryption in patient data analytics, collaborative research, and AI-driven diagnostics for hospitals, research centers, and pharmaceutical companies.

North America was the Dominating Region in the Market; Asia Pacific is Emerging as the Fastest Growing Region in the Market over 2025-2032

The global Homomorphic Encryption market was led by the North America region in 2024, with more than 42.30% market share, owing to the presence of major technology players operating in this market including IBM, Microsoft, and Google, among others. The Homomorphic Encryption market in the Asia-Pacific region is anticipated to show the highest CAGR of 8.08% during the forecast period due to the rising digitization as well as the adoption of cloud computing.  

Recent Developments:

• In April 2024, Microsoft released updates to its SEAL (Simple Encrypted Arithmetic Library) homomorphic encryption library, enhancing its capabilities for secure computations on encrypted data.
• In October 2024, Google introduced the Homomorphic Encryption Intermediate Representation (HEIR) compiler, aiming to simplify the development of homomorphic encryption applications.

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Exclusive Sections of the Report (The USPs):

• Real-World Computation Statistics – helps you assess the operational efficiency of homomorphic encryption by tracking encrypted query throughput, AI/ML training success rates on encrypted datasets, and computational performance across FHE, partial, and somewhat HE methods.
• Data Sensitivity Metrics – helps you understand the scale and depth of adoption across highly regulated sectors (BFSI, Healthcare, Government) by analyzing the number of sensitive records encrypted and the total data volume processed without decryption.
• Innovation & Research Metrics – helps you evaluate the pace of technological advancement through annual algorithmic breakthroughs, latency reduction achievements via hardware/cloud accelerators, and the intensity of academic–corporate collaborations in HE innovation.
• Security Impact Metrics – helps you quantify the tangible cybersecurity benefits by measuring the number of security incidents prevented, average encryption duration during computation, and overall proportion of sensitive datasets processed fully encrypted.
• Ai Model Training Efficiency Index – helps you benchmark how effectively AI and ML models are being trained on encrypted datasets compared to unencrypted ones, offering insights into practical readiness for confidential AI deployments.
• Resource Optimization Benchmarks – helps you evaluate the trade-offs in CPU, GPU, and memory usage across HE variants, guiding infrastructure scaling and cost optimization decisions for enterprise deployments.

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