Researchers at the University of Copenhagen, led by Aydogan Ozcan, have developed a groundbreaking software that employs a unique method to protect sensitive data, crucial for machine learning in health care applications. This innovative approach ensures the privacy of individuals while making datasets accessible for the development of improved medical treatments.
In modern healthcare, analyzing data from a large patient group is essential for uncovering patterns and determining treatment effectiveness. However, ensuring the privacy of such data is paramount to maintain public trust and consent. The research team, based at the Department of Computer Science, addresses this challenge with a practical and cost-effective solution.
The new algorithm introduces a concept of deliberately adding "noise" to any output derived from the dataset. Unlike traditional encryption methods, this noise remains a permanent part of the output, making it impossible to distinguish from the true output. This strategy protects privacy by lowering the utility of the dataset, ensuring the anonymity of participants.
Ph.D. student Joel Daniel Andersson, the lead researcher, highlights the importance of finding the right trade-off between privacy and utility. For applications where privacy is critical, such as healthcare data, a high level of privacy is achievable by adding a substantial amount of noise. However, this may necessitate an increase in the number of data points to maintain the dataset's value.
The method's key innovation lies in its ability to add less noise, requiring fewer computational resources, thereby reducing the associated costs of ensuring privacy. The researchers anticipate broad applications in various sectors, including health care, large tech companies like Google, and industries such as consulting, auditing, and law firms that handle sensitive client data.
Differential privacy, the underlying principle of the method, ensures datasets' privacy by making outputs indistinguishable when differing in a single data point. The research group advocates for public regulation in this field to establish minimum privacy standards for sensitive applications. Joel Daniel Andersson emphasizes the adaptability of differential privacy, allowing users to choose the required level of privacy while offering a probabilistic guarantee against privacy violations.
In conclusion, the University of Copenhagen's breakthrough in differential privacy not only safeguards individual privacy in machine learning applications but also presents a promising avenue for societal benefit by facilitating increased data participation in crucial research areas like medical surveys.
