SecAppDev 2020 Lecture Details

Protective optimization technologies

Seda Gürses
Tuesday March 10, 16:00 - 17:30
Short description

Businesses nowadays can design systems for "ideal" interactions and environments by optimizing systems using machine learning and AI. However, these strategies have costs and associated risks. We will talk about these risks and costs, and introduce Protective Optimization Technologies as a way to flag or mitigate them.

Abstract

The transformation to service-oriented architectures (SOA) and agile development as the dominant modes for producing software has given rise to a new type of system: optimization systems. Businesses nowadays can design systems for "ideal" interactions and environments by optimizing feature selection, behavioral outcomes, resource allocation and planning that is in line with a business growth strategy. Unfortunately, the advantages often outshine the negative impact and risks of these systems.

This session offers an introduction to optimization systems, their risks and harms, and possibilities of using (adversarial) machine learning as a way to protect affected people and their environments.

Key takeaway

Machine learning and AI have significant business advantages, but come with harms and risks that require thinking beyond privacy and security.

Content level

Introductory

Target audience

Anyone working with big data systems and behavioral analysis.

Prerequisites

None


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Seda Gürses

Seda Gürses

Assistant professor, TU Delft

Seda is currently an Assistant Professor in the Department of Multi-Actor Systems at TU Delft, and an affiliate at the COSIC Group at KU Leuven. Previously she was an FWO post-doctoral fellow at COSIC/ESAT, and a research associate at the Center for IT and Policy at Princeton University. Her work focuses on privacy enhancing and protective optimization technologies (PETs and POTs), privacy engineering, as well as questions around software infrastructures, social justice and political economy.

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