Privacy-enhancing technologies (PETs) are essential for protecting sensitive data while enabling meaningful analysis and operational efficiency. This guide introduces several key PETs, offering an overview of how each technology uniquely balances privacy with practical functionality across diverse applications. By enabling secure data processing, encrypted computations, and private collaboration, these technologies allow organisations to preserve individual privacy and maximise the benefits of data-driven insights.
Homomorphic encryption
Homomorphic encryption is a form of encryption that allows computations to be performed on data while it is still encrypted. This means that, unlike traditional encryption, which requires data to be decrypted before processing, homomorphic encryption enables computations on encrypted data without exposing it. As a result, the output remains encrypted and can only be decrypted by someone with the correct decryption key. This type of encryption is particularly useful in environments where data privacy is paramount but processing on the data is still required, such as in cloud computing or with sensitive financial and healthcare information.
For example, consider a bank that holds encrypted financial records in the cloud. With homomorphic encryption, the bank can perform necessary calculations—such as interest calculations or aggregating customer balances—on these encrypted records without ever exposing sensitive data to the cloud provider. Only the bank, with the decryption key, can decrypt the results and access the computed data in plaintext. This way, data privacy is preserved while still allowing the flexibility to perform essential operations on the data remotely. Homomorphic encryption thus offers a solution to balancing privacy with functionality in data processing scenarios.
Secure multiparty computation
Secure multiparty computation (SMPC) is a cryptographic technique that allows multiple parties to jointly compute a function over their inputs while keeping those inputs private from each other. In SMPC, each party’s data remains confidential, and only the result of the computation is revealed. This is useful in scenarios where parties need to collaborate to compute a shared output but do not trust each other enough to share their private data. Through various cryptographic protocols, such as secret sharing and zero-knowledge proofs, SMPC ensures that no party gains any knowledge of the other parties’ inputs beyond what is strictly necessary for the computation.
A practical example of SMPC is its application in collaborative data analysis for companies competing in the same industry. Suppose two companies, A and B, want to analyse shared sales data to understand market trends but do not want to reveal their individual sales data to each other. Using SMPC, they can jointly compute aggregated sales statistics without exposing their specific sales figures. For instance, they might compute the average sales across both companies or identify popular product categories collectively. Through SMPC, both companies gain useful insights from the analysis while preserving the confidentiality of their proprietary data, fostering collaboration without compromising privacy.
Private set intersection
Private set intersection (PSI) is a cryptographic protocol that allows two or more parties to determine the intersection of their datasets without revealing any other information about their data. In other words, each party learns only the common elements between their sets and nothing about the elements that are unique to the other parties’ sets. PSI is particularly useful in scenarios where multiple parties need to find common data points but wish to keep their non-intersecting data private. This is achieved through cryptographic techniques that enable this intersection calculation securely, protecting each party’s unique data from exposure.
An example of PSI can be seen in collaborations between hospitals or research institutions working on disease outbreaks. Suppose two hospitals have patient databases and want to identify individuals who have visited both facilities to track the spread of an infectious disease. Using PSI, they can compare their patient lists to find overlapping patients without revealing their entire datasets to each other. Only the names of patients who appear in both lists are revealed to each hospital, and any other patient information remains confidential. This approach allows them to share critical, overlapping data needed for public health purposes while respecting patient privacy and confidentiality requirements.
Federated learning
Federated learning is a machine learning approach that enables multiple devices or servers to collaboratively train a shared model without sharing their actual data. In traditional machine learning, data is typically centralised on a single server for training, but with federated learning, the data remains decentralised on individual devices or servers, such as mobile phones or local databases. Each participant trains the model locally on its data, and only the learned model updates (not the raw data) are shared with a central server. These updates are then aggregated to improve the global model. This approach enhances privacy, as sensitive data never leaves the local device, making federated learning suitable for privacy-sensitive applications.
A practical example of federated learning is its use in predictive text or keyboard suggestion models on smartphones. Each phone collects data on how users type, which could improve suggestions over time. However, sending all user typing data to a central server would raise significant privacy concerns. With federated learning, each phone trains the predictive text model on its user’s data locally. Only model updates (i.e., patterns or trends learned without specific data points) are sent to a central server, where they are combined to improve the global model. This way, the central model benefits from learning across all devices without exposing individual user data, making predictive text both more effective and privacy-preserving.
Trusted execution environments
Trusted execution environments (TEEs) are secure areas within a device’s main processor that provide an isolated environment for executing code and processing data securely. They operate separately from the regular operating system, creating a protected space where sensitive computations can occur without interference or exposure to the rest of the system. TEEs leverage hardware-based security features to ensure that data confidentiality and integrity are maintained, even if the main operating system is compromised. This isolation is critical for applications that handle sensitive information, such as encryption keys, passwords, or personal biometric data.
A common example of TEE usage is in modern smartphones for secure biometric authentication. When a user enrolls their fingerprint or facial data, this sensitive information is processed and stored within the TEE. Later, when the user attempts to unlock the phone using biometrics, the comparison between the stored data and the new input happens inside the TEE. This means that the biometric data never leaves the secure environment, protecting it from potential attacks by malicious software on the device. By utilising TEEs in this way, smartphone manufacturers enhance the overall security of the device, ensuring that personal data remains protected even if other parts of the system are compromised.
Zero-knowledge proofs
Zero-knowledge proofs (ZKPs) are cryptographic methods that allow one party, the “prover,” to demonstrate to another party, the “verifier,” that they possess certain knowledge or meet certain criteria without revealing any additional information beyond the fact that the statement is true. In a zero-knowledge proof, the verifier gains confidence that the prover knows or possesses the secret information without actually learning the secret itself. This is achieved through complex mathematical protocols that confirm the truth of a statement without divulging specifics. ZKPs are valuable for enhancing privacy and security in various applications, as they enable secure verification without revealing sensitive data.
One practical example of zero-knowledge proofs is in verifying a user’s identity without revealing sensitive personal information, such as in online voting. Using ZKPs, a voter could prove that they are an eligible participant in an election (e.g., proving they are over 18 and a citizen) without revealing any identifying details like their birthdate or address. The voting system can verify the legitimacy of the voter’s eligibility through a zero-knowledge proof, allowing the voter to cast their vote anonymously. This application preserves the privacy of individual voters while maintaining the integrity of the election process, ensuring that only eligible voters participate without exposing their personal information.
Differential privacy
Differential privacy is a statistical technique designed to protect individual privacy when analysing and sharing aggregate data. It works by adding a controlled amount of random noise to data or computations, ensuring that the inclusion or exclusion of any single data point does not significantly affect the output. This means that it is nearly impossible to determine whether a particular individual’s data is present in a dataset based on the analysis results. Differential privacy is especially useful for organisations that want to share or analyse data from large groups without risking the exposure of any one individual’s information, providing both utility and strong privacy guarantees.
A practical example of differential privacy is in the analysis of population data by a government statistical agency. For instance, if a census bureau wants to publish statistics about income distribution across regions without exposing individual incomes, it could apply differential privacy. By adding random noise to the published income data, the bureau ensures that individual data points cannot be reverse-engineered, even if someone has access to additional data about some individuals. This approach enables the bureau to release useful, accurate statistics for policy-making and research purposes while safeguarding the privacy of citizens’ personal information. Differential privacy thus allows valuable insights to be derived from data while protecting individual privacy in the process.
Synthetic data
Synthetic data is artificially generated data that mimics the statistical properties of real-world data, created through algorithms, simulations, or generative models. Unlike real data, which is collected from actual events or interactions, synthetic data is produced to represent similar patterns and distributions without directly replicating actual data points. This makes synthetic data particularly useful in situations where privacy, data availability, or scalability are concerns, as it allows for the analysis and testing of models without exposing or depending on sensitive or limited real-world data. It is commonly used in machine learning, data science, and other fields that require large datasets for training and testing.
A practical example of synthetic data is in healthcare, where patient data is often restricted due to privacy regulations like HIPAA. Researchers developing a machine learning model to predict patient outcomes might use synthetic data that reflects the real distribution of patient characteristics—such as age, medical history, and treatment responses—without using actual patient records. By training and validating their models on this synthetic dataset, researchers can optimise algorithms and assess their effectiveness in a way that respects patient privacy. Once the model is refined, it can be applied to real data under controlled and secure conditions, ensuring that patient confidentiality is maintained throughout the research and development process.
Summary
In conclusion, privacy-enhancing technologies (PETs) play a pivotal role in balancing data utility and privacy across various sectors. By employing advanced cryptographic techniques, isolated processing environments, and data anonymisation methods, PETs enable secure and efficient data processing without compromising individual or organisational confidentiality.
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