Table of Contents
- Purpose of the Report
- Introduction to Privacy-Enhancing Technologies (PETs)
- Types of PETs and Plain-Language Explanations: A Glossary
- Key Considerations for Decision-Making
- Combining PETs to Maximize Utility and Privacy
- Practical Considerations and Barriers to PET Adoption
- Advancing the Use of Privacy-Enhancing Technologies
- Conclusion
- Appendix 1. Key Term Definitions
- Appendix 2. Key Evaluation Questions for Privacy-Enhancing Technologies (PETs)
Appendix 1. Key Term Definitions
- Algorithm: a step-by-step set of instructions a computer follows to solve a problem or perform a task.
- Cryptographic method: a technique used to secure data by transforming it into a format that is unreadable to unauthorized users, often used to protect sensitive information.
- Data aggregation: the process of collecting and summarizing data from multiple sources to form a comprehensive set or report, often used for analysis and reporting.
- Data de-identification: the process of removing or modifying personally identifiable information from datasets, so that individuals cannot be readily identified from the data, while maintaining its utility for analysis.
- Data integrity: the accuracy and reliability of data.
- Data lifecycle: the stages through which data passes from creation or collection, through processing and analysis, to sharing, storage, and eventual deletion or archival.
- Data processor: a person or company that handles data on behalf of another organization.
- Data sharing: the practice of making data available for access, use, or collaboration with other parties.
- Input data: the information that goes into a system (i.e., a search term you type into Google).
- Machine learning model: a type of computer program that learns patterns from data and makes predictions or decisions without being explicitly programmed.
- Noise: random data or alterations deliberately introduced into a dataset to protect individual privacy by preventing re-identification, commonly used in techniques like differential privacy.
- Operating system: the software that runs on a computer or phone and manages all its basic functions, like running apps, storing files, and connecting to the internet.
- Output data: the result that comes back from a search (i.e., the list of results you see).
- Risk of exposure: the likelihood or potential for sensitive data to be accessed, disclosed, or misused by unauthorized individuals, systems, or entities.
- Security measures: the technical, administrative, and physical actions taken to safeguard data against unauthorized access, alteration, destruction, or theft.
- Sensitive information: any data in need of extra protection due to its confidential nature, such as health records, financial details, or Social Security numbers that carry more harm if exposed.
- Server: a powerful computer that stores and processes data and makes it available to other devices. When visiting a website, for example, you’re getting information from a server.
- Statistical properties: the characteristics of data, such as averages, trends, and distributions, that can be used to understand patterns without revealing specific personal details.
- Transaction: the process of transferring or exchanging data between parties, such as in financial exchanges or when querying databases.
- Unintended disclosure: the accidental or inadvertent disclosure of sensitive data, potentially due to technical flaws or human error.