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Privacy enhancing technology (PET) is a term used to describe any tool, software, or hardware that helps protect user privacy. Fortunately, we have privacy enhancing technologies (PETs) to help us protect our data online and offline. Patrick J. Austin is a cybersecurity and data privacy attorney working on complex regulatory and compliance matters, in addition to enterprise risk management. If you’re looking to implement PETs as a component of your data security program, please get in touch.
CategoriesSelect CategoryArtificial IntelligenceBUZZCloud & VirtualizationCloud & Virtualization DifferencesCollaborationConfig & TroubleshootDifferencesHardware, Infrastructure & DesignInfra & Design DifferencesInterview QuestionsIT & BusinessProduct ReviewProgramming & SoftwareProtocolProtocols DifferencesRouting & SwitchingRouting https://www.m-sedan.com/general_driving_tips-4421.html & Switching DifferencesSecuritySecurity DifferencesServices and ApplicationsStorageWireless IPwithease is aimed at sharing knowledge across varied domains like Network, Security, Virtualization, Software, Wireless, etc. By understanding how these powerful tools work together, we can ensure our patient’s confidential information remains safe while still allowing them access to the healthcare services they need when they need them most. We also looked at some of the best privacy enhancing technologies available today and how they can help protect your online and offline data.
For example, PETs are routinely being deployed and implemented on an array of tech platforms and other AdTech partners, which means digital marketing teams and advertisers will ultimately need to embrace and adopt these privacy-enhancing technologies. With federated learning, each office could effectively process personal data locally on its devices while still contributing to the AI model’s update. Such an organization could utilize federated learning as a PET to aid in the training of an AI model that involves using data collected from different offices within the larger organization. An example of how PETs can be deployed for data collaboration can be found in multinational organizations with operations in the U.S., the EU, and other countries.
Why Privacy Enhancing Technologies Matter in 2026
We recently acquired Addingwell, a server-side tracking and tagging platform, and have joined forces with Sourcepoint, a data privacy technology pioneer with strong publisher market share and over 200 global enterprise customers. Over the next 5 to 10 years, there is a realistic possibility that PETs become part of the default architecture for different types of software, services, and so forth. However, it is important to understand that PETs are not perfect and have some inherent limitations.
Accelerating data monetization and partnership marketing
Secure multiparty computation relies on cryptographic protocols involving encryption and mathematical techniques to enable multiple parties to jointly collaborate on computations involving personal data. This can help organizations by ensuring data privacy while still engaging in meaningful data analysis with encrypted information. Homomorphic encryption allows for computations to occur on encrypted data without having to engage in any decryption. Similarly, Waymo utilized synthetic data to develop realistic driving data sets to train its self-driving vehicle systems. In effect, synthetic data can aid organizations in mitigating the risk of data exposure while conducting effective data analyses and developing machine learning models. By using synthetic data, organizations can help safeguard sensitive PII by generating synthetic datasets.
This blog post explored PETs and how they work by providing secure storage and sharing options for sensitive medical information. Some popular examples include Secure Sockets Layer (SSL), Virtual Private Networks (VPN), two-factor authentication (2FA) systems such as Google Authenticator or Authy, Zero Knowledge Proof protocols like Zcash, Homomorphic Encryption (HE) systems https://mycosesstudygroup.org/educatio/EventDetails.pl?slno=399 like Microsoft SEAL library, as well as distributed ledger technology such as Blockchain networks like Ethereum or Hyperledger Fabric. For example, many PETs employ encryption algorithms that scramble the data so it cannot be read by anyone who does not have access to the key used for unlocking it. This is where PETs come in; they allow us to securely store and share sensitive medical information without compromising patient confidentiality. PETs use encryption algorithms and other techniques to protect against unauthorized access to sensitive data, including personal information or health records.
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- They get accurate tourism statistics without any individual phone data being exposed—not even to government officials.
- Financial services create anonymized datasets for fraud detection development.
- International companies can share synthetic datasets across borders without worrying about local privacy laws.
- Intel's SGX technology creates these protected areas where sensitive calculations happen.
PETs, particularly secure multiparty computation and federated learning, can be utilized to ensure secure data collaboration without needlessly exposing sensitive personal information. This is important because many organizations need to collaborate on matters with third parties (e.g., service providers, partners, research organizations, etc.) that may entail the sharing of personal data. Utilizing PETs can help organizations to more efficiently and securely manage personal data without having to process and store unnecessary or excessive personal information. The use of PETs can assist an organization in developing secure data processing protocols, maintaining data privacy, and ensuring compliance with U.S. and global data privacy regulations.
- You need people who understand both the technology and your business requirements.
- They can optimize public transportation and plan infrastructure based on citizen movement patterns without tracking individual residents.
- The system learns traffic patterns while keeping specific routes private.
- Researchers are identifying early warning signs that could save lives, but no individual patient information ever gets shared.
- Companies are discovering that strong privacy practices actually improve customer relationships and enable new business models.
Data Minimization
Synthetic data enables organizations to generate artificial data that mirrors real-world personal data, while still preserving important data privacy protections. Provides the ability to collaborate with organizations to train AI models or engage in research projects without sharing or revealing sensitive personal data. Rather than sending raw data to a central server, only model updates (also known as gradients) are communicated, which can help bolster the overall privacy protections of collected and processed personal data. Specifically, differential privacy is focused on determining the precise amount of randomness that is necessary to achieve statistical privacy protection.
Helps manage how personal data is structured within particular environments to ensure access is controlled, while maintaining the integrity and confidentiality of sensitive personal data. They can be utilized to protect code and data within them from external tampering, even from the operating system or other software layers. These environments provide a secure and isolated area for executing sensitive code or operations. Trusted execution environments are secure hardware or software environments within a computer system.