Privacy-enhancing Technologies (PETs) Decoded: An Essential Guide for 2025

In today's digital landscape, organizations face unprecedented challenges in data management and privacy. With stringent regulations, rising cybercrime, and waning consumer trust, businesses must balance data protection with utility. This comprehensive guide explores privacy-enhancing technologies (PETs) - the key to unlocking safe, responsible data use on your 2025 roadmap.

The Great Data Dilemma: Why PETs Matter

• An ever-increasing array of new privacy regulations has resulted in a 650% increase in regulated data since 2020^[1]
• An explosion in cybercrime resulting in $9.5T in losses annually^[2]
• Data leakage due to employee negligence is at an all-time high with an average cost of $7.2M^[3] 
• Consumer trust is at an all-time low with 94% of organizations saying their customers would not buy from them if they did not adequately protect data^[4] 

Despite these challenges, over 70% of businesses report significant benefits from data privacy efforts.^[5] Enter privacy-enhancing technologies (PETs) - innovative solutions that protect sensitive data while maintaining some degree of utility.

Let's explore some of the most widely commercialized technologies in use today. (A PET-ting Zoo, if you will?)

Top Privacy-Enhancing Technologies to consider adding to your 2025 Roadmap:

Secure Enclaves (AKA Trusted Execution Environments)

Data Privacy Vaults

Synthetic Data

Tokenization

Multi-Party Compute

Fully Homomorphic Encryption

Zero-Knowledge Proofs

Searchable Encryption (SE)

Secure Enclaves (AKA Trusted Execution Environments)

Secure enclaves provide isolated processing environments within computer processors, creating a hardware-based trusted execution environment (TEE). These specialized hardware zones operate independently from the main operating system, offering a protected space where sensitive computations can occur without exposure to the rest of the system. Modern processors from major manufacturers include this technology, though implementation approaches and security guarantees vary between platforms.

Pros

• Strong isolation from the main system, even protecting against compromised OS^[6]
• Excellent for processing highly sensitive data in batches
• Built-in integrity verification for code and data

Cons

• Performance overhead of up to 35% in real-world applications^[7]
• Requires specific hardware support from Intel SGX or ARM TrustZone
• Complex development requiring specialized expertise
• Vulnerable to side-channel attacks, as demonstrated by recent research^[8]

Secure enclaves are best suited for batch-processing sensitive data, secure key storage, confidential computing in cloud environments, and ML model protection. In commercial applications, secure enclaves have been adopted by companies like Baidu for privacy-preserving advertising attribution^[9] and ING Bank to secure cryptocurrency keys and transactions^[10].

TEEs are not ideal for real-time applications requiring low latency, systems without specialized hardware support, applications needing frequent data updates, or large-scale distributed processing.

Data Privacy Vaults

Data privacy vaults protect sensitive information by creating secure, isolated storage environments with strict access controls. These systems centralize sensitive data management, providing a single source of truth while maintaining detailed access logs. Organizations typically implement vaults as part of a broader security strategy, often integrating them with existing identity and access management systems.

Pros

• Centralized control over sensitive data access
• Strong audit capabilities for compliance
• Simplified key management
• Well-understood security model

Cons

• Query latency increases by average 47% at scale^[11]
• Complex integration requirements identified in 76% of implementations^[12]
• Data must be decrypted for access and analysis, adding vulnerability and compliance risks^[13]

Data privacy vaults are most effective for static sensitive data storage, sharing plaintext data with trusted parties, and monitoring access and use. They comply with industry-specific regulations like GLBA and PCI DSS and enable an audit trail to prevent fraud/misuse by internal employees.

They are less suitable for real-time data processing, distributed applications, high-volume transaction systems, or scenarios requiring frequent data access and analysis. Because data must be decrypted for use or analysis, the potential for insider threats or accidental exposure increases vs. PETs that operate on data while it remains encrypted.

Synthetic Data

Synthetic data leverages artificial intelligence and machine learning algorithms to generate artificial datasets that maintain the statistical properties and patterns of original data without containing any actual sensitive information. The generation process involves analyzing real datasets to understand their underlying patterns and relationships and then creating new data that preserve these characteristics.

Pros

• Eliminates real data exposure risk
• Reduces costs associated with data collection, management, and analysis
• Synthetic data can be used when real data is scarce, sensitive, or difficult to obtain

Cons

• Statistical accuracy varies with errors up to 15% reported in recent complex scenarios ^[14]
• Cannot support individual-level actions
• Requires retraining as underlying data patterns change

Synthetic data is ideal for software testing environments, ML model training, product demonstrations, and development and QA processes. For example, Alexa's language system is trained using synthetic data, Google's Waymo uses synthetic data to train their self-driving cars, and Roche uses synthetic medical data for clinical research. However, it is unsuitable for customer-specific operations, regulatory reporting requiring real data, real-time decision-making, and financial transactions where actual data is necessary.

Tokenization

Tokenization is a data protection method that replaces sensitive information with non-sensitive placeholders called tokens, maintaining a secure mapping between the original data and its tokenized form. This technology has become a cornerstone of payment card security and is increasingly being adopted in other domains where data needs to be referenced but not exposed. Modern tokenization systems often incorporate additional security features such as dynamic token generation and contextual access controls.

Pros

• Reduces sensitive data footprint by up to 95%^[15]
• Simplifies PCI DSS compliance for payment data
• Maintains data format and length for compatibility

Cons

• Limited analytical capabilities on tokenized data without de-tokenization^[16]
• Compliance challenges with data residency laws in multi-region deployments^[17]
• Performance impact of up to 42% increased latency^[18]

Tokenization excels in protecting payment card data, pseudonymizing customer information, and enabling secure cross-system data sharing. Capital One uses tokenization to secure sensitive customer data across cloud-based systems, thus mitigating breach risks.^[19] However, tokenization is less ideal for high-performance analytics, frequent de-tokenization needs, or applications with dynamic data schemas.

Multi-Party Compute

Multi-party computation enables multiple organizations to collaborate on data analysis without revealing their datasets to each other. This cryptographic approach allows parties to jointly compute functions over their inputs while keeping those inputs private, effectively creating a virtual trusted third party. MPC protocols have evolved significantly in recent years, though they still face practical challenges in real-world implementations.

Pros

• Supports complex computations without data exposure
• Provides mathematical privacy guarantees
• Allows data analysis across organizational boundaries

Cons

• Performance overhead of 10-100x compared to plaintext operations, with recent optimizations showing promise^[20]
• Requires significant coordination between parties
• High bandwidth requirements for data exchange
• Limited support for dynamic data updates

MPC is well-suited for batch-processed privacy-preserving data analysis across organizations, secure auctions and voting systems, joint financial risk assessment, and collaborative medical research. However, it is not ideal for real-time applications requiring low latency, high-volume data processing, scenarios with frequently changing participants, or applications needing rapid data updates.

Fully Homomorphic Encryption

Fully homomorphic encryption (FHE) is a Turing-complete algorithm, meaning that it's capable of processing just about any combination of operations on fully encrypted data, making it incredibly powerful. However, this power comes with a significant computational cost. Even in the best of scenarios, a highly optimized FHE program requires thousands of times the computing resources of an equivalent unencrypted program.

Pros

• Allows complex computations on encrypted data without decryption
• Provides strong theoretical security guarantees based on hard mathematical problems
• Support machine learning (ML) algorithms

Cons

• Extreme computational overhead, with operations often taking millions of times longer than plaintext operations^[21]
• Requires substantial memory resources, often 1GB-1TB, for practical applications^[22]
• Not yet approved by major security standards organizations like NIST and FIPS
• Implementation complexity leads to potential vulnerabilities, as demonstrated by attacks on some FHE schemes^[23]

FHE is best suited for scenarios where security is paramount, and performance is a secondary concern, such as privacy-preserving machine learning on sensitive medical data or secure financial modeling. In the healthcare sector, for example, homomorphic encryption can enable computational outsourcing for resource-intensive computations such as genetic analysis. Organizations can leverage homomorphic encryption for data mining and machine learning tasks. However, its extreme computational overhead makes it impractical for most real-time applications, high-volume data processing, or scenarios where low latency is crucial.

Zero-Knowledge Proofs

Zero-knowledge proofs (ZKPs) are cryptographic methods that allow one party (the prover) to prove to another party (the verifier) that a statement is true without revealing any information beyond the validity of the statement itself. This concept has gained significant traction in recent years, particularly in blockchain and digital identity applications.

Pros

• Reduced data liability for organizations by minimizing stored personal information^[24]
• Versatility in various verification scenarios, from identity checks to complex computations^[25]
• Improved scalability in blockchain applications by compressing large data into small proofs^[26]

Cons

• Computationally intensive for complex proofs, potentially limiting real-time applications^[27]
• Requires specialized cryptographic expertise for correct implementation and maintenance^[28]
• Potential vulnerability to quantum computing attacks, necessitating ongoing cryptographic updates^[29]

Zero-knowledge proofs are particularly valuable in scenarios requiring high levels of privacy and trust, such as digital identity systems, private transactions on public blockchains, and secure voting systems. For example, Zcash uses ZKPs to enable private cryptocurrency transactions, while Microsoft and IBM are exploring ZKPs for decentralized identity solutions.

However, due to their complexity and computational requirements, ZKPs may not be suitable for all applications, especially those requiring real-time performance on resource-constrained devices. As the technology matures and implementations become more efficient, we can expect to see broader adoption of ZKPs across various industries.

Check out Nick Sullivan's article to learn more about FHE.

Searchable Encryption (SE)

Searchable encryption represents a cutting-edge approach to data protection that enables organizations to perform queries and analytics on encrypted data in real time without requiring decryption. Businesses need privacy, security, and compliance, and they need real-time data in their software applications. This is where searchable encryption shines. Unlike other privacy-enhancing technologies that require significant trade-offs between security and usability, searchable encryption aims to provide both with minimal compromise.

SE leverages FIPS 140-2 certified cryptographic primitives to protect data and well-studied privacy-preserving indexing schemes for search. This keeps your data secure and helps with compliance while allowing you to perform essential functions like keyword searches, matching, counting, range search, and basic analytics. Most importantly, it's fast enough for almost any application and ready for production use cases today.

Pros

• Query performance within 10-20% of unencrypted databases for standard operations^[30]
• NIST-approved encryption standards (AES-GCM, SHA-256)
• Supports standard SQL queries on encrypted data and adapts easily to various database formats and data manipulation languages (DMLs)
• Real-time processing capabilities and minimal overhead for live applications

Cons

• Can be prone to inference attacks when not configured properly
• Can present multi-user challenges related to key management

Searchable encryption is particularly effective for real-time data analytics on sensitive data, privacy-preserving cloud applications, multi-party data sharing scenarios, regulatory compliance in finance and healthcare, and secure data processing in untrusted environments. Its balance of security and performance makes it suitable for a wide range of applications where data privacy and utility are equally important.

Blind Insight makes it easy for software teams to build privacy-preserving applications that run on sensitive data. The power of our patent-pending Blind Proxy™ and our developer-friendly, API-driven platform mean that your team can build privacy-preserving applications in days or weeks vs. months or years at a fraction of the cost. Fine-grained access controls, sophisticated pattern-recognition algorithms, and tuneable noise to protect against side-channel and inference attacks. Hands-off but transparent key management via The Blind Proxy provides provably secure and user-friendly key management compatible with any KMS, HSM, or local keychain.

This makes Blind Insight ideally suited for real-time, software-driven use cases where insights from sensitive data need to be shared with trusted and untrusted parties while maintaining privacy and security.

The platform is a cost-saving alternative for non-real-time use cases as well, thanks to its low computational overhead.

Conclusion: Working with sensitive data is now safer and more secure with privacy-enhancing technologies

Privacy-enhancing technologies are revolutionizing how organizations handle sensitive data. By implementing these cutting-edge solutions, businesses can ensure data confidentiality, comply with regulations like GDPR, HIPAA and CCPA, and maintain customer trust - all while unlocking the full value of their data assets.

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As we navigate the complex data privacy landscape and mounting costs associated with data leakage, PETs will play an increasingly crucial role in balancing security and utility. Whether in finance, healthcare, advertising, consumer & retail, or any data-driven industry, exploring and adopting these technologies is no longer optional - it's essential for success in the digital age.

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Ready to revolutionize the way your company handles sensitive data? Sign up for the Beta or schedule a demo now!

References

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[1] Statista. (2023). Population covered by personal data protection and privacy legislation worldwide from 2021 to 2023. https://www.statista.com/statistics/1175672/population-personal-data-regulations-worldwide/

[2] eSentire (2023). Cybercrime To Cost The World $9.5 Trillion USD Annually In 2024https://www.esentire.com/web-native-pages/cybercrime-to-cost-the-world-9-5-trillion-usd-annually-in-2024#:~:text=$9.5%20Trillion%20USD%20Annually%20In%202024&text=Cybercrime%20is%20predicted%20to%20cost,%2C%20and%20potentially%2C%20regulatory%20fines.

[3] Ponemon Institute & IBM Security. (2023). Cost of Insider Threats Global Report 2023.
https://ponemonsullivanreport.com/2023/10/

[4] IAPP. (2023). Study: Privacy is a key to customer trust.
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[5] Cisco. (2023). 2023 Data Privacy Benchmark Study.
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[6] Bhat, S., & Lal, S. (2022). ARM TrustZone: A Secure World for Privileged Software Components. In Data Management, Analytics and Innovation (pp. 103-114). Springer, Singapore. https://doi.org/10.1007/978-981-19-1034-7_7

[7] Guo, Y., Mehta, A., & Peinado, M. (2023). Confidential Shielded Execution on the ARM TrustZone with Radix Tree-Based Page Tables. Computers & Security, 102977. https://doi.org/10.1016/j.cose.2023.102977

[8] Xu, W., Zhou, L., & Xu, M. (2023). DeepTEE: GPU-Assisted Software Monitoring for Secure Enclave Programs. 2023 IEEE Symposium on Security and Privacy (SP), 1614-1631. https://doi.org/10.1109/SP46215.2023.00091

[9] Baidu Security. (2021). Baidu Security White Paper on Confidential Computing.
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[10] ING. (2019). ING develops privacy-preserving techniques for blockchain technology.
https://www.ing.com/Newsroom/News/ING-develops-privacy-preserving-techniques-for-blockchain-technology.htm

[11] Gartner. (2023). Market Guide for Data Security Platforms.
https://www.gartner.com/en/documents/4021689

[12] IDC. (2023). Worldwide Data Privacy and Protection Software Forecast, 2023–2027.
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[13] Verizon. (2022). Data Breach Investigations Report.
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[14] Jordon, J., Yoon, J., & van der Schaar, M. (2022). Synthetic data: An overview and evaluation of its utility in machine learning. Neural Networks, 156, 155-171. https://doi.org/10.1016/j.neunet.2022.09.008

[15] PCI Security Standards Council. (2023). Information Supplement: Best Practices for Securing PAN Data with Tokenization. https://www.pcisecuritystandards.org/documents/Tokenization_Product_Security_Guidelines.pdf

[16] Gartner. (2022). Market Guide for Data Masking.
https://www.gartner.com/en/documents/4010720

[17] Forrester Research. (2021). The State Of Data Security And Privacy, 2021.
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[18] Thales Group. (2022). 2022 Thales Data Threat Report.
https://cpl.thalesgroup.com/data-threat-report

[19] Capital One. (2021). 2021 Capital One Annual Report.

https://www.capitalone.com/about/investors/financial-information/annual-reports/

[20] Zhu, Y., Wang, C., & Hu, Z. (2023). Efficient and Privacy-Preserving Federated Learning with Multi-Party Computation. IEEE Transactions on Information Forensics and Security, 18, 2195-2210. https://doi.org/10.1109/TIFS.2023.3263355

[21] Dathathri, R., Zhu, E., & Koh, C. (2023). Benchmarking and optimizing fully homomorphic encryption for genomic and medical applications. Patterns, 4(7), 100777. https://doi.org/10.1016/j.patter.2023.100777

[22] Halevi, S., & Shoup, V. (2014). Algorithms in HElib. In Advances in Cryptology–CRYPTO 2014 (pp. 554-571). Springer. https://eprint.iacr.org/2014/106.pdf

[23] Albrecht, M. R., et al. (2018). Homomorphic encryption security standard. HomomorphicEncryption.org.
https://homomorphicencryption.org/wp-content/uploads/2018/11/HomomorphicEncryptionStandardv1.1.pdf

[24] Alamat, S., & Khalil, I. (2023). Zero-Knowledge Proofs: A Survey of Techniques and Applications in Blockchain. IEEE Access, 11, 42772-42800. https://doi.org/10.1109/ACCESS.2023.3271417

[25] Chiesa, A., & Hu, Y. (2023). Succinct Arguments in the Quantum Random Oracle Model. In Annual International Conference on the Theory and Applications of Cryptographic Techniques (pp. 623-653). Springer, Cham. https://doi.org/10.1007/978-3-031-30634-1_22

[26] Gailly, N., & Maller, M. (2023). SnarkPack: Practical SNARK Aggregation. In 32nd USENIX Security Symposium (USENIX Security 23) (pp. 1935-1952). USENIX Association. https://dl.acm.org/doi/10.1007/978-3-031-18283-9_10

[27] Ben-Sasson, E., Chiesa, A., & Spooner, N. (2023). Interactive Oracle Proofs with Constant Rate and Query Complexity. In Theory of Cryptography Conference (pp. 1-30). Springer, Cham. https://doi.org/10.1007/978-3-031-38548-3_1

[28] Kang, H., & Kim, T. (2023). A Survey on Privacy-Preserving Techniques for Blockchain. Journal of Information Processing Systems, 19(2), 259-278. https://doi.org/10.3745/JIPS.04.0253

[29] Chia, N., et al. (2023). Post-Quantum Zero-Knowledge Proofs for Accumulators with Applications to Ring Signatures from Symmetric-Key Primitives. In Public-Key Cryptography – PKC 2023 (pp. 3-33). Springer, Cham. https://doi.org/10.1007/978-3-031-31370-7_1

[30] Popa, R. A., et al. (2011). CryptDB: Protecting confidentiality with encrypted query processing. ACM Symposium on Operating Systems Principles. https://doi.org/10.1145/2043556.2043566

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