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AI-Driven Privacy Enhancing Technologies (PETs) and Clarifying Misconceptions About It


PETs (privacy-enhancing technologies) have become an essential tool for protecting our personal data, and the combination of PETs and AI is changing the game. But these advancements are frequently obscured by misunderstandings and falsehoods. In this blog, we will examine how artificial intelligence (AI) plays a crucial role in the development and implementation of PETs to protect our digital privacy.

Understanding PETs

The majority of PETs fit into one of the following five categories: homomorphic encryption, secure multi-party computation, federated learning, differential privacy, and trusted execution environment. Each has particular advantages and is used for different things, from enhancing privacy to releasing the full potential of data.

Let’s take a closer look at each of them.

1. Homomorphic Encryption

Homomorphic encryption enables calculations to be carried out on data while it is still encrypted. As a result, the data will continue to be secure and will only be accessible to those who have authorization.


Applications for homomorphic encryption range from cloud computing and analytics to data exchange and storage. It is a useful tool for businesses that need to manage and distribute massive datasets securely while still enabling analytics and other uses for the information.

2. Secure Multi-Party Computation (SMPC)

A particular kind of PET called SMPC enables many parties to collaboratively compute a function while maintaining the privacy of each party’s data. For businesses that must cooperate and exchange data with numerous stakeholders while protecting data privacy, it is an exceptionally helpful tool.

When several parties must work together without disclosing sensitive information, such as in medical research, secure multi-party computation is used. It is widely utilized in fields like banking and finance, where it supports data secrecy while enabling collaborative analysis and decision-making.

3. Federated Learning

Federated learning is a type of machine learning that enables the training of machine learning models across multiple decentralized devices without transferring the raw data to a central server.

Each device uses its own local data to train the model, and only aggregate updates are sent to the central server. Due to the raw data being on the devices and never being shared, this helps protect data privacy.

IoT or mobile app situations involving connected devices and distributed data benefit from federated learning. In comparison to models based on centralized data, it is utilized to create customized models that are more accurate.

4. Differential Privacy

Differential privacy is more of a mathematical definition of privacy than it is a technology that enhances privacy. The privacy leakage that happens when examining a differentially private database is quantified by differential privacy. The epsilon value is the name of this measurement. In a perfect world, or with an epsilon value of 0, the outcome of the aforementioned analysis would be the same regardless of whether a specific person is included in the database or not.

Epsilon values above a certain point increase the risk of privacy leaking. Epsilon values below 1 are advised in academics to achieve robust anonymization. Finding a good epsilon value in practice is still difficult. It’s crucial to keep in mind that differential privacy does not always ensure sufficient privacy protection. It only provides a mathematical assurance for the maximum allowable privacy leakage. Therefore, it is crucial to set the epsilon value correctly. In order to ensure privacy, it must be low enough, but not too low that the noise that must be added to attain this low epsilon number reduces the usefulness of the data.


Practitioners of privacy utilize it more frequently in conjunction with another PET, such as federated learning. With the help of noise addition to datasets, differential privacy is excellent at hiding patterns. Some banks mask synthetic data with additional differential privacy to keep confidential business insights away from prying eyes.

5. Trusted Execution Environment

Another crucial component of PETs is the Trusted Execution Environment (TEE). In order to protect the confidentiality and integrity of data and code, it offers a secure and isolated processing environment within a computer system where delicate activities are carried out separately from the main processor and memory.

Organizations may safeguard crucial activities and computations, including secure data processing, key management, and cryptographic operations, by using TEE. The business that Syntheticus partners with, CYSEC, specializes in offering trusted execution environments for containers and aids in the deployment and security of sensitive data across dispersed architectures, from data centers to the cloud and the edge.

Leveraging PETs for Privacy and Data Utilization

Organizations greatly benefit from PETs’ capacity to safeguard individuals’ privacy while facilitating data use. By guaranteeing adherence to data ethics standards and privacy laws, they facilitate ethical AI research, machine learning, and advanced analytics.

In order to find the correct balance between privacy and data use, many businesses have already adopted PETs. For instance, retailers use PETs to examine customer purchase patterns without gaining access to personal information. This strategy allows for focused marketing initiatives while protecting personal privacy.

PETs allow secure patient data collaboration for research while safeguarding private medical data in the healthcare industry. In the banking industry, PETs support fraud detection and guarantee the privacy of consumer information while doing data analysis. These are just a few instances of how PETs are used in a variety of industries to protect privacy and improve data use.

PETs are frequently used in conjunction with one another, fusing various strategies to maximize data use and privacy protection. For instance, Syntheticus offers a full solution that includes Trusted Execution Environment, differential privacy approaches, and synthetic data generation through a partnership with CYSEC. This potent combination guarantees not only the preservation of the data’s accuracy and privacy but also the secure execution of crucial processes within a reliable environment.

Nevertheless, the difficulties involved in putting PETs into practice might occasionally feel overwhelming, especially in the beginning. Some businesses might not have the capacity to create or execute their own solutions, while others can find it challenging to embrace new PETs in an efficient manner due to outdated systems and procedures. Working with a dependable partner who can give them access to the necessary resources and knowledge is frequently the best course of action for enterprises trying to harness PETs.

Choosing the Right PET: The Tree Approach

The data lifecycle and the context relevant to the industry should be the deciding factors in choosing which PET to employ. Companies can use a useful framework known as “the tree approach” to make this choice, which takes into account the data type, purpose, and context when choosing the best PET.

However, you must first establish your purpose and objectives as well as the data type before using the tree strategy. Consider the responses to the following inquiries as you proceed:

1. What kind of data do you handle, and what degree of privacy protection is necessary?

2. For what purpose are you utilizing this information?

3. How does it fit into the framework of stakeholder requirements and legislation relevant to the industry?

Tree Approach

You will be able to identify the type of data, its function, and its legal limitations by responding to these questions. The appropriate PET is then matched to your data and use case using this information.

Data, scenario, context, and solution are the four unique levels that make up the tree approach.

The first level, data, deals with the different sorts of data that businesses handle, such as private health information, sensitive financial data, or personally identifiable information, as well as the appropriate level of privacy protection.

The second level, or scenario, describes the objectives and use cases for which the data is required, such as the testing and debugging of new features or the training of machine learning models.

Contextual level three takes industry-specific rules and stakeholder needs into account. Making sure that the chosen PET conforms with the many privacy laws and regulations that may apply in different locations is essential.

The fourth level, referred to as the solution, deals with the various PETs that are available, such as anonymization methods and the fabrication of synthetic data, as well as their relative efficacy in relation to the data, scenario, and context.

The Centre for Data Ethics and Innovation PETs adoption guide, which offers a visual representation of the “tree approach” that helps them select the most suitable solution, can be used by organizations to assist them in choosing the best PET for their organization.

The benefits and drawbacks of various privacy-enhancing technologies must be carefully considered. The majority of enterprises will need to invest in more than one PET to cover all use cases, while some of them are more use-case neutral than others. Although their usage should be restricted, several traditional anonymization methods may also have a role in the data tech stack as supplemental measures.


Classification of the Misconceptions about Modern Privacy Tech

Privacy tech primarily serves the needs of compliance and legal teams

Reality: PETs are not just for the legal or compliance teams. They provide many advantages outside of regulatory compliance. By embracing privacy, security, and governance by design, they facilitate data monetization, secure AI development, and joint initiatives like medical research or financial compliance (KYC and AML). PETs unlock data usefulness and collaboration, irrespective of data source or location.

Security holds greater significance compared to privacy tech

Reality: Wrong. Governance, security, and privacy must all cohabit from the outset. Furthermore, privacy technologies close a critical hole in data security: protecting data while it is being used. For instance, all agreements involving third parties’ data recognize incidental access to data and place ‘confidence’ in them to have adequate safeguards in place.  Instead of requiring such faith, PETs provide technical safeguards to guarantee that only the data owners have access to the raw data while allowing analyzing parties to carry out computations and build models using the data.

Myth 3: Privacy Techs are too slow

Reality: PETs are not slow; in fact, by removing time-consuming manual operations, they speed up processes. Technology developments have sped up and improved privacy technology. For instance, privacy-enabled systems can offer answers in seconds in situations like KYC, where the time it takes for queries to be answered is frequently months. With PETs, international data collaboration has accelerated noticeably.

Privacy compromises data quality

Reality: Data quality is not compromised by PETs. They safeguard privacy while maintaining data quality, unlike manual deidentification approaches. Case studies, such as those carried out with organizations like Tel Aviv Medical Center, have shown that insights obtained using PETs can fulfill privacy rules like the UK GDPR while meeting or exceeding accuracy standards compared to clear data analysis.

Is it similar to tokenization?

Reality: PETs are not the same as tokenization. PETs completely do away with the requirement for deidentification, whereas tokenization largely focuses on tying together de-identified data sets. They use cutting-edge encryption techniques to protect data while in use and adhere to international privacy standards. In order to give data owners governance controls to manage inquiries, computations, and access, PETs entail software installs at the locations of the data owners and analyzers.


When combined with AI, privacy-enhancing technologies offer potent solutions for protecting sensitive data while dispelling common misconceptions about them. They are crucial tools for safeguarding data privacy, boosting security, and encouraging responsible data governance; they are not only for compliance or legal teams. To stay ahead and assure a secure and privacy-conscious digital future as we traverse the changing terrain of data privacy, adopting AI-powered PETs is crucial. They are absolutely necessary in today’s data-driven environment because of the advantages they provide, such as improved privacy and accelerated innovation. PETs give businesses a variety of ways to safeguard customer information while maximizing data use. Organizations may guarantee a secure and privacy-preserving data infrastructure by comprehending the various types of data and use cases, evaluating regulatory requirements, and choosing the appropriate PET for their business. 

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