Strict privacy laws limit your ability to share and use data for research, testing, and development. This is why privacy-enhancing technologies are critical for any business, small and large, as they’re designed to help comply with privacy and data protection regulations.
But the fact is, not everyone understands what privacy-enhancing technologies are and how they differ from other security tools. If that includes you — you’ve come to the right place.
This article will describe different privacy-enhancing technologies, examples of use cases for businesses, and their potential advantages. We will also help you choose the right type of PET for your organization.
Your guide into synthetic data generation
PET (privacy-enhancing technology) encompasses tools that help protect personally identifiable information (PII) and minimize security risks. The examples of privacy-enhancing technologies include software, algorithms, methodologies, and physical components (like hardware keys).
PETs are essential for businesses that must responsibly handle sensitive customer and corporate data, as well as comply with data privacy regulations without compromising functionality. They can safeguard information for testing, development, research, or service-improvement.
While compliance is the primary goal of PETs, companies implement them for different reasons.
Businesses deal with datasets that contain a lot of sensitive information. Adopting PETs can prevent many privacy-related problems and unlock several business advantages.
Not all PETs keep your data equally protected or ensured. It depends on the type of tool and technique employed.
PETs include various techniques for privacy and data protection. Let’s describe the most common methods that are accessible for businesses today.
Homomorphic encryption allows computations on encrypted data without needing to decrypt it. When decrypted, encrypted data will match the outcome of operations on the original data. This cryptographic technique maintains data privacy for data processing, analysis, and sharing.
The main issue with this technique is its computational complexity, as operations on encrypted data are slower than on plaintext. Implementing homomorphic encryption also requires advanced cryptographic expertise.
SMPC (or just MCP) allows multiple parties to compute a function using inputs and view a public output while preserving data confidentiality. Companies, researchers, and users can aggregate and analyze values from multiple data sources without privacy compromises.
Like homomorphic encryption, secure multi-party computation introduces computational overhead and requires significant processing power. Besides, for SMPC to work, multiple parties should have a mutual trust network and compatible infrastructure.
Differential privacy is a mathematical framework that introduces controlled randomness (noise) into datasets to the real PII. The primary advantage of differential privacy is that you can measure the level of privacy. You can add the exact amount of noise to maintain the data utility. Still, companies need expertise to avoid producing inaccurate or misleading data.
ZKP is a cryptographic verification method that allows one party to prove that they possess knowledge about data without revealing its contents. The verifier cannot access or modify the original input — it can only understand if the statement is valid.
This method is primarily used for transaction verifications. It also requires intense processing power to generate and verify proofs.
Data masking techniques include removing, altering, or obfuscating data to safeguard sensitive information. These techniques allow organizations to use realistic data for testing, development, and research. Examples of privacy-enhancing technologies of data masking include:
However, most of these techniques come with compromises. Usually, you have to combine masking with other privacy-enhancing technologies to prevent the risk of re-identification.
Federated learning is a decentralized machine learning technique in which models are trained across multiple locations. Each device trains the model locally and only shares the updated parameters, which are used to upgrade the model.
While very secure, federated learning is very complex when coordinating across devices. If not properly secured, the aggregated model parameters are also vulnerable to attacks.
TEE (or Secure Enclave) is a physically isolated location, usually within a primary processor, that safeguards code and data from operating systems and other applications. It’s a hardware-based PET where you can store and execute code without the risks of unauthorized access and malware.
However, the security and scalability of the TEE largely depend on the capabilities of your processor hardware. If malicious actors discover hardware vulnerabilities, you risk compromising the entire environment.
Synthetic data is fully generated and compliant information that mimics real-world data. AI-generated datasets don’t contain personal data or indirect identifiers, exempting them from data privacy laws. In other words, you are free to use and share this data without regulatory oversights.
Synthetic data generation is one of the most confidential privacy-enhancing technologies. Unlike most anonymization and data masking methods, synthetic data platforms preserve the structural relationships in data, making it suitable for advanced research and development.
Synthetic data requires sophisticated algorithms to generate data that accurately represents the actual data. That’s why companies should only choose a reputable synthetic platform.
Companies usually employ several PETs to ensure efficient data governance and data confidentiality. But, in addition to technologies, companies should commit to efficient practices.
A few organizational-level strategies are necessary to make PETs more efficient and ensure data confidentiality.
Following these practices will make implementation of data privacy-enhancing technologies easier in the long run.
Incorporating these practices into your data governance strategy will help you leverage PETs to enhance privacy, security, and data utility.
Begin by auditing all personal data your organization collects and classifying it based on sensitivity. Create a prioritization matrix based on the data’s sensitivity, ranking types of data based on risk level. Most companies want to incorporate PETS gradually, so this will help you focus on the technologies that add the most value first.
Then, outline the common data use cases into specific scenarios. For example, if you aim to secure customer data, identify the types of customer interactions that require protection (like transactions, ad targeting, or customer support conversations).
Analyze your current IT infrastructure, communication protocols, and data formats. This will help you understand the compatibility requirements for your new PETs. Prioritize the technologies that require minimal changes to your setup. It also helps to try platforms with a free trial to see how well they integrate with your toolset.
Compare PET providers based on your needs for privacy, usability, and performance. For example, your team can manually analyze the accuracy of anonymized datasets against the real data or try to re-identify the data back to the original form.
Assess the long-term viability of the PETs, considering the maintenance requirements, potential technical debt, and scalability. Make sure you can migrate the data and workflow to other platforms.
Evaluate the total cost of ownership for each PET. This requires you to compare the direct subscription or license costs, as well as expenses for integration, training, or the adoption period.
If you need to get the higher management onboard, calculate the expected return on investment for each PET. The costs are qualitative (like enhanced customer trust and less regulatory oversight) and quantitative (like fewer losses from data breaches).
Look for vendors with customer reviews and testimonials. Focus on what other companies say about the service support level, implementation challenges, and ease of training. Look for specific examples of how vendors have addressed challenges similar to yours. A reliable partner will help you set up the PET by providing integration assistance, training, and comprehensive support.
Privacy-enhancing technologies (PETs) are downright critical for privacy and data protection. However, not all PETs can make your data fully compliant or maintain data usability for advanced research. You should combine several techniques and practices to ensure data confidentiality and security.
Synthetic data generation is an excellent way to get high-quality, privacy-first data that mirrors real data. Syntho’s smart synthetic data generation platform allows you to create such data on a whim for various use cases, from healthcare research to algorithm training.
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