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A Data Scientist’s Guide to Preserving Privacy
March 20, 2019 @ 5:30 pm - 8:30 pm
This meetup is with Columbus Data Science. In order to keep an accurate RSVP list, please sign up at the link below.
5:30 — Arrival, networking, food
6:00 — Presentation begins
7:00 — Discussion
As personal data becomes abundant, the risk of sensitive data being leaked or misappropriated has become much greater. This risk is greatly increased by the ability to augment publicly available data. This occurs, in part, because aggregation erodes privacy — the combination of disparate and seemingly minor bits of personal information can be used to infer sensitive personal attributes. Consequently, organizations seeking to maintain trust with their customers must have robust frameworks in place to preserve privacy not only with their own internal data but also when those sources are joined to external data.
In this talk, we will give an overview of approaches to maintain privacy as well as highlight the vulnerabilities of each approach within analytics workflows. We will review three common techniques for data privatization: masking, k-anonymization, and differential privacy. For each technique, we will focus the discussion around a case study that highlights the trade-offs between data utility, privacy preservation, and robustness against linkage attacks.
Attendees will walk away with a framework for identifying privacy risks in their own analyses, multiple approaches that can be used to preserve privacy, and how to make decisions balance utility and privacy.
About the Speakers:
Alfred Rossi is a Research Scientist, and Stephen Bailey is a Data Scientist, at Immuta. Immuta is the fastest way for algorithm-driven enterprises to accelerate the development and control of machine learning and advanced analytics. The company’s data management platform provides data scientists with rapid, personalized data access to dramatically improve the creation, deployment and auditability of machine learning and AI.
Food and drink will be provided.