The COVID-19 pandemic has underscored the critical role of data in various fields, including research, cause analysis, governmental decision-making, and advancements in medical science. However, the necessity to protect sensitive information often leads to a reluctance among individuals and decision-makers to share personal data. This poses a significant challenge as we strive for continuous progress. To overcome this, we need to adopt innovative practices that allow us to glean insights from personal data while ensuring the stringent protection of individual privacy.
In response to this need, a groundbreaking approach known as differential privacy has emerged. This method was pioneered by a team of researchers at Microsoft, in collaboration with the OpenDP Initiative led by Harvard University. Differential privacy is rapidly becoming the gold standard for data protection, particularly in applications that involve the preparation and publication of statistical analyses.
Differential privacy works by adding a carefully calibrated amount of statistical noise to sensitive data. This process provides a mathematically quantifiable privacy guarantee to individuals. Compared to traditional disclosure limitation practices such as data anonymization, differential privacy offers significantly enhanced levels of privacy protection.
This white paper aims to provide practical guidance on how differential privacy can be employed to rigorously protect personal data. It is particularly relevant for applications in statistics, machine learning, and deep learning. By understanding and implementing differential privacy, we can ensure that personal data is used responsibly and securely, thereby fostering trust and promoting further advancements in these fields.