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Securing the e-Campus 2017 - Exact time and dates TBD

Recent Talks

Dan Wallach

STAR-Vote: A Secure, Transparent, Auditable and Reliable Voting System

Professor Dan Wallach
Rice University
Thursday April 27, 2017
Carson L01, 5:00 PM

Ben Miller Dragos

Pandora's Power Grid - What Can State Attacks Do and What Would be the Impact?

Ben Miller
Chief Threat Officer, Dragos, Inc.
Tuesday May 2, 2017
Kemeny 007, 4:30 PM
Brendan Nyhan




Factual Echo Chambers? Fact-checking and Fake News in Election 2016.

Professor Brendan Nyhan
Dartmouth College
Thursday May 4, 2017
Rocky 001, 5:00 PM

Dickie George


Espionage and Intelligence

Professor Dickie George
Johns Hopkins University
Thursday May 11, 2017
Rocky 001, 5:00 PM

Dan Wallach

A Nation Under Attack: Advanced Cyber-Attacks in Ukraine

Ukrainian Cybersecurity Researchers
Thursday April 6, 2017
Oopik Auditorium 5:30 PM

RIOTS logo 

Professor Sean Smith, Director of the ISTS and Bill Nisen, Associate Director, spoke at the

School House residential cluster on the Internet of Risky Things  - February 21, 2017, 5:30 PM

Craig Smith




You Don't Own Your Car
Craig Smith
Tuesday May 10, 2016 
Carson L02 @4:15

David Safford


Hardware Based Security for GE's Industrial Control Systems
David Safford
GE Global Research
Tuesday May 17, 2016
Carson L02 @4:15



"It's Fine," They Said. "Just Ship It," They Said.
Dan Tentler
The Phobos Group
Tuesday April 12, 2016 
Carson L02 @4:15

Harold Thimbleby




The Best Way to Improve Healthcare is to Improve Computers
Harold Thimbleby
Swansea University
April 23, 2015

Craig Shue




Managing User-Level Compromises in Enterprise Network
Craig Shue
Worcester Polytechnic Institute
March 31, 2015



Oct news 2015


ISTS Information Pamphlet



Institute for Security, Technology, and Society
Dartmouth College
6211 Sudikoff Laboratory
Hanover, NH 03755 USA

Discovery of Trends in Activity-Aware Computing Environments

Project Summary

The use of mobile sensors is becoming increasingly commonplace in the everyday lives of people. Examples of sensors range from accelerometers, cameras and microphones on cell phones to special-purpose devices that capture physiological (e.g., EKG, GSR) or contextual information (e.g., temperature, location). These sensors can be used to automatically infer a range of human behavioral states (e.g., physical activities, emotional states, social interactions, and activities of daily living such as cooking or cleaning), the knowledge of which would be useful in a variety of applications, such as detecting anomalous and suspicious behavior, supporting emergency response efforts, tracking physical and cognitive health of individuals, and enhancing the user experience of social and communication technologies.

Much of the activity recognition research has so far focused on the analysis of one individual's sensor data in isolation. However, understanding the trends in activity patterns requires the analysis to move beyond individual to groups, which we believe will not only enable a broader range of applications but also reveal the privacy issues that are present in these scenarios. For example, can an individual be uniquely identified by his activities? Or are there enough similarities across people that can be exploited to anonymize a person's identity? In this project, we will focus on developing algorithms for discovering activity trends, deriving quantitative metrics for finding people who are behaviorally alike, and identifying possible strategies to address some of the privacy concerns that this research uncovers. Furthermore, we will explore the use of sensors on commercially available sensor-equipped mobile devices (such as the iPhone) and the possibility of tying some of our results into popular social applications (such as Facebook or ContextAwareIM).

Last Updated: 9/9/15