Data Security and Privacy in the Age of Machine Learning

ABOUT THE PANEL: Personally Identifiable Information (PII) is piling up in databases and on filesystems across the globe. Smart companies are hard at work generating insights from this data, while World-dominating companies are intentionally generating it, mining it and in various ways obtaining clear value from it. GDPR and HIPPA are game-changing government regulations affecting data storage and transmission, while, in the meantime, advances in machine and deep Learning are powering huge leaps in analytical insights and business innovation.

In addition, an unlevel playing field exists between the sheer size of the data accumulated at the biggest tech cos vs. the nimbleness and inspiration of the smallest startups. Yes, companies of all sizes are competing with each other in an attempt to add significant value to their users.

At best, large datasets represent the bedrock for meaningful consumer insights; value-added customer features, services, and products; and massive amounts of rich training data to increase model efficiency. At worst, new systems, algorithms and data architectures represent a plethora of nefarious new opportunities to de-anonymize, leak or blatantly distribute data that was previously secret and/or obfuscated.

So in this brave new world of data and algos and regulations what are the privacy concerns surrounding data access and security?

Our panelists will explore these issues, from the hands-on perspective of building some of the most sophisticated data mining systems in the world. They are all hands-on technologists - data scientists, engineers, researchers and technical founders - and will share from their deep experience in building massively scalable data systems. They will also help us contemplate the thorny issues of technical and ethical responsibility - issues essential to consider as we all work together to build the data-driven systems of the present, and the future.

ABOUT THE SPEAKERS:

Soups Ranjan heads Financial Crime Risk at Revolut, the fastest growing challenger bank in Europe. He leads the team in charge of preventing financial crime on Revolut’s platform using data science and machine learning. At Coinbase, Soups built many engineering teams from the ground up including data, risk and identity. Soups is the co-founder of RiskSalon.org, a roundtable forum for risk professionals in San Francisco and Seattle to share ideas on stopping financial crime. Soups holds a PhD in ECE focused on network security from Rice University. Soups currently lives in Berkeley with his family.

Praneeth Vepakomma is currently a grad student and researcher at MIT in Camera Culture research group where his focus is on developing algorithms to support distributed and collaborative machine learning. He was previously a scientist at Amazon, Motorola Solutions and at various startups all of which were successfully acquired.

Arun Krishnaswamy is a lead data scientist at workday, where he is focusing on building next gen threat intelligence platform, empowering the security team’s capabilities to fight fraud at scale. Arun has extensive technology and management experience—he has built teams from scratch at multiple startups and at VISA, Microsoft , Yahoo. Prior to joining Workday, Arun worked as a Senior Director at One Market focused on building ML Solutions combining physical and digital assets of a shopper and worked extensively around customer privacy issues.

Rupa Parameswaran is a seasoned security professional with a passion for data protection and privacy. Her interest in data privacy dates back to graduate school where she chose privacy-preserving data mining as her PhD research topic and graduated with a PhD thesis on privacy-preserving data mining techniques for recommendation systems. She has been active in the field for over 15 years with a combination of research and industry experience in the area of security, data protection, and privacy.

Abe Gong is the Founder and CEO at Superconductive Health and a core contributor to the Great Expectations open source library. Prior to Superconductive, Abe was Chief Data Officer at Aspire Health, the founding member of the Jawbone data science team, and lead data scientist at Massive Health. Abe has been leading teams using data and technology to solve problems in health care, consumer wellness, and public policy for over a decade. Abe earned his PhD at the University of Michigan in Public Policy, Political Science, and Complex Systems. He speaks and writes regularly on data, healthcare, and data ethics.


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