NITI Aayog & Perlin Hackathon Winner

Announcing the winning team for our global privacy preserving AI competition

After months reviewing hundreds of worthy submissions from all around the world, the Perlin team and judges are happy to announce the final stage winner of our global hackathon: Ryan McKay and team from Australia.

Ryan and his team made an impressive submission for the application of privacy preserving techniques in AI for health-related private data, with their submission and demo entitled: Differential Privacy and Directional Noise Applied to Large Synthetic Medical Patient Data.

One of the key goals of the Hackathon was to explore ways AI can be used to derive meaningful and valuable insights from the massive datasets provided by huge populations of individuals, while also protecting the privacy of that personal data. The ability to do this in a practical and scalable way will allow researchers and organizations around the world to find meaning in the vast and ever-growing stores of data on every facet of life. For example, personal health data previously not available for privacy reasons would now be usable to do a wide range of very useful things things, such as: identifying critical trends in the health of populations, supporting R&D to develop new and targeted cures for diseases based on the specific needs and characteristics of communities (geographic distribution, socio-economic status, demographic profile, etc), creating more relevant and efficient healthcare services, and more.

Insights drawn from big data can help industries like healthcare provide better, cheaper and more relevant products and services to people in need.

Currently, the vast majority of data remains siloed or inaccessible due to privacy concerns. By freeing up this data, all markets, industries and communities can benefit from a better understanding of people’s actual needs and behaviours based on the data collected. This could be as simple as the data from our browser histories to the more complex ways in which we interact with governments, companies and each other. The potential use-cases are endless and across all sectors and industries — ranging from helping governments to better understand their constituencies, to informing commercial enterprises seeking to improve their products and services based on dynamic changes in consumer needs and preferences.

Finding a workable solution that opens up vast amounts of data and keeps our personal data strictly private will transform societies by informing and driving more focused, efficient and relevant innovation in every conceivable area of modern life. Our hope at Perlin is that solutions like that developed by Ryan and his team will help get us one step closer.

The Perlin team is excited to work more with Ryan and his team to further develop and potentially implement his solution in the real world, in collaboration with our partners like the Indian Government and global AI-focused companies.


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’Til Next Time

Darren Toh