Data breaches have become rampant, with hacking and unauthorized data sharing by companies leading to spiraling privacy invasions. As organizations strive to extract insights from consumer data, regulations like GDPR and CCPA have imposed stiff penalties for violations – over $1.2 billion in fines under GDPR alone as of January 2022 according to DLA Piper.
Implementing privacy enhancing technologies (PETs) is no longer just a compliance checkbox – it has become a strategic imperative for mitigating risk and enabling sustainable data-driven growth. In this comprehensive guide, we will overview the top 10 PETs to know in 2024 and prime use cases across industries.
What are Privacy Enhancing Technologies?
Privacy enhancing technologies (PETs) refer to the diverse range of software and hardware solutions designed to process personal data securely without compromising privacy.
PETs enable data analysis, sharing, and application testing without exposing raw sensitive attributes. They empower organizations to unlock the full potential of data while upholding individual privacy rights.
Why are PETs Critical Now?
There are three converging factors making PET adoption more crucial than ever:
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Data protection laws: Regulations like GDPR and CCPA impose serious fines for violations – over $1.2 billion and counting under GDPR since 2018.
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Third-party sharing: Most companies lack full in-house analytics capabilities and need to share data with vendors, enabled securely via PETs.
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Reputational harm: Privacy breaches diminish consumer trust and severely damage brand reputation (ex. Facebook after Cambridge Analytica).
| Year | GDPR Fines (EUR) | # of Fines | Avg. Fine Size (EUR) |
|---|---|---|---|
| 2018 | 56M | 91 | 615K |
| 2019 | 114M | 160 | 713K |
| 2020 | 192M | 215 | 893K |
| 2021 | 1.1B | 535 | 2M |
GDPR fines data via DLA Piper GDPR Fines & Data Breach Survey 2022
PET adoption is accelerating from a "nice to have" to a must-have. Let‘s explore the top PET categories and examples to consider.
Top 10 Privacy Enhancing Technologies
Here I‘ll provide an overview of 10 leading PET types with examples, pros/cons, and implementation guidance based on my experience.
1. Homomorphic Encryption
Homomorphic encryption enables computation on encrypted data without decryption. The encrypted outputs can be returned and decrypted to reveal useful results from operations performed on the original data. There are three main types:
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Partially homomorphic – Allows one type of operation (addition or multiplication) on encrypted data. Least computationally intensive.
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Somewhat homomorphic – Allows limited operations on encrypted data. Moderately computationally intensive.
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Fully homomorphic – Allows unlimited operations on encrypted data. Most computationally intensive.
Examples: Microsoft SEAL, Duality, GPU-accelerated HElib.
Pros: Allows sensitive data sharing and outsourced analytics without exposing raw data.
Cons: Can be computationally expensive, especially fully homomorphic encryption.
Use When: Sharing identifiable data with untrusted third parties.
2. Secure Multi-party Computation
SMPC is a form of homomorphic encryption for sharing data from multiple encrypted sources. It enables collaborative machine learning on combined datasets while preserving privacy.
Participants supply encrypted input and receive encrypted output to decrypt locally. No raw data is ever centralized or visible.
Examples: Partisia, Inpher, Sharemind
Pros: Enables analytics and ML across siloed, distributed data.
Cons: Computationally intense with multiple data sources.
Use When: Cross-organizational collaborative analytics.
3. Differential Privacy
Differential privacy protects individual identities by adding controlled statistical noise to datasets before analysis and release. It enables learning aggregate patterns about a population without exposing any one person‘s data.
Examples: Google RAPPOR, US Census Bureau applications
Pros: Strong privacy protection for individuals in dataset release.
Cons: Some loss in analytic accuracy due to noise.
Use When: Publishing statistical data derived from sensitive sources.
4. Zero-Knowledge Proofs
Zero-knowledge proofs allow sensitive data to be validated without revealing the actual private contents. The data owner provides a "proof" it is correct without sharing the underlying info.
Examples: zk-STARKs, zk-SNARKs
Pros: Provides verification without disclosing private data.
Cons: Set-up can be complex for some protocols.
Use When: Validating compliance or credentials without visibility.
5. Data Obfuscation
Obfuscation refers to masking original sensitive data by adding misleading, confusing, or misleading information. Techniques include:
- Encrypting/masking certain data fields
- Shuffling data records
- Adding fake entries
- Deleting or masking original values
Examples: Privacy Analytics, VERASCO
Pros: Directly protects sensitive attributes and IDs.
Cons: Can reduce utility if applied too broadly.
Use When: Limiting internal employee data access.
6. Pseudonymization
Pseudonymization replaces directly identifying information like names or IDs with artificial substitutes like pseudonyms or surrogate keys. This renders the data less identifiable while preserving analytic utility.
Examples: Hashing personally identifiable information
Pros: Reduces identifiability of data.
Cons: Subject to re-identification if used improperly.
Use When: GDPR compliance, internal analytics.
7. Data Minimization
Data minimization means limiting collected personal information to only what is directly relevant and necessary to accomplish a specific purpose. It enhances privacy by reducing unnecessary data exposure.
Pros: Limits vulnerability surface by collecting less data.
Cons: Can inhibit certain analytics use cases.
Use When: Collecting user data across applications.
8. Communication Anonymizers
Anonymizers provide disposable, untraceable digital identities like email addresses or IP addresses to mask real user identities. They enable anonymous secure web browsing and communication.
Examples: Tor browser, DuckDuckGo email protection
Pros: Provides anonymous communication and transactions.
Cons: Can also enable cybercrime if used maliciously.
Use When: Communicating/browsing anonymously.
9. Synthetic Data Generation
Synthetic data is artificially generated to simulate real data while not containing actual personal info. It maintains statistical features of real datasets for realistic modeling, testing, etc.
Examples: Causam, MOSTLY AI, LexSet
Pros: Enables testing/sharing without privacy risk.
Cons: May poorly approximate real data distributions in some cases.
Use When: Anonymizing data for third party testing.
10. Federated Learning
Federated learning distributes a shared ML model to decentralized devices that train it on local data, sending only updates rather than raw data to a centralized server. Enhances privacy protection in collaborative analytics applications.
Examples: Google Federated Learning, PyTorch Federated Learning
Pros: Train ML models collaboratively without sharing raw training data.
Cons: Model accuracy can degrade compared to centralized training.
Use When: Building ML models from sensitive, distributed data sources.
Evaluating Privacy Enhancing Technologies
With many PET options available, I recommend organizations consider these factors when evaluating solutions:
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Data sensitivity – Assess the level of confidentiality of various data types you collect and process to determine the privacy protection required.
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Use cases – Identify your specific intended analytics, testing, and data sharing use cases to select the appropriate PET capabilities.
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Implementation complexity – Evaluate your in-house skills and bandwidth to deploy and manage more sophisticated PETs requiring specialized expertise, like homomorphic encryption.
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Cost – Compare licensing, development, and operational costs across shortlisted PET platforms.
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Compliance – Ensure the PET supplies necessary proof to demonstrate regulatory compliance.
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Security & performance – Vet third party code, audit trails, key management practices. Benchmark performance overhead.
Thoroughly trial shortlisted PETs on sample data to choose the optimal solution.
Best Practices for PET Adoption
Based on my experience implementing PETs across various industries, here are some best practices:
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Start with lower sensitivity data – Pilot PETs on a sample dataset to test capabilities before expanding to higher sensitivity data.
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Layer solutions – Use multiple complementary PETs for defense-in-depth rather than relying on just one.
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Limit data access – Only share minimum necessary PET-protected datasets with authorized personnel.
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Maintain audit trails – Log PET usage and keep detailed audit records, especially for regulated data like financial or healthcare.
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Retrain models – Data transformations via PETs may require retraining ML models on the modified datasets.
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Customize over generalized PETs – Leverage customizable platforms over pre-built, generic PET products for greater control.
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Partner with PET experts – Work with reputable PET vendors providing support services and custom integrations.
Top Privacy Enhancing Use Cases
Now let‘s explore some of the major applications and use cases where PETs deliver high value:
Financial Services
Financial institutions rely on PETs to share and analyze sensitive customer data like transactions, investments, and credit information securely with internal and external systems.
Regulations mandate strict controls around consumer financial data sharing. Homomorphic encryption and MPC enable institutions to extract insights across accounts without exposing raw data.
Healthcare
Healthcare organizations need to share patient diagnostic data, prescriptions, medical histories, and more with various providers while maintaining compliance with regulations like HIPAA.
PET techniques like differential privacy and synthetic data generation are instrumental for analyzing healthcare trends and progressing research while deidentifying protected health information.
Retail & Advertising
Retailers collect vast amounts of consumer data to deliver personalized recommendations and optimize operations. Ad networks rely on consumer insights to target advertising. PETs help prevent undesired profiling while enabling product improvements based on buyer trends.
Government
Government agencies like census bureaus hold citizen data that must be protected. PETs allow deriving population statistics to inform policy decisions without exposing individual-level records. Governments are major users of differentially private analytics.
Based on your specific industry‘s data sensitivity, sharing needs, and regulatory obligations, there is likely a PET approach that can help unlock data‘s value while protecting privacy.
Conclusion
Privacy enhancing technologies empower organizations across sectors to fully capitalize on data‘s potential while safeguarding sensitive information and upholding privacy rights. As regulations expand and data volumes explode, PET adoption is accelerating from "nice to have" to "must have".
Leading solutions like homomorphic encryption, zero-knowledge proofs, and federated learning protect consumers while enabling groundbreaking research, product innovations, and data monetization opportunities.
Businesses should thoroughly evaluate options based on data sensitivity, intended analytics, compliance needs, and in-house capabilities. With the right privacy enhancing technologies in place, companies can drive growth through data while strengthening consumer trust and loyalty.