Surveillance, Sousveillance, and Security Technologies: A variety of wearable computing devices (2019)

Curators: Patrice Banks and Laura Burnes| University of Minnesota | December 2019

Collection Editor: Isabel Pedersen
Acquisitions Editor: Ann Hill Duin
Archivists: Sharon Caldwell and Jack Narine

This Curated Collection involves case studies of related digital artifacts that focus on security and surveillance technologies, including wearable devices and their evolving mechanics. However, it also includes artifacts that describe how some devices perform “sousveillance” as a counter to organizational surveillance.

There is a demographic trend at work that affects social practices.  To accommodate millennials’ fierce attachment to digital devices and their expectation that they should be able to work and function wherever with flexible security, wearables and gadgets are available to keep personal. information safe, and in some instances, ward off surveillance. 

Some of the contributions examine the rise in custom-build surveillance systems designed to meet corporations’, governments’, and individuals’ unique security needs. Revolutionary advances in machine learning and Artificial Intelligence (AI) have helped advance these systems. As we encounter new technologies, security is an area that needs to quickly evolve to keep up with the advancements in deep learning and other disciplines in the technology space. Security teams have embraced the millennial shift with increasing numbers of on-the-go security measures, and expanded boundaries to secure data. Furthermore, the growth of cloud and mobile technologies generate more networks to secure and more data to protect. 

Wearable hardware powered by AI inventions can identify, track, or analyze people in real time for unsolicited information, and with both positive and negative effects. Some have grown skeptical of security-protected guarantees from companies and organizations.  More specific, body-worn surveillance gear is becoming popular as some can enable a person to capture surveillance while on the move or protect one’s privacy from the latest facial recognition technology.

While we typically think about deep learning in a positive context, some technology models are vulnerable to sophisticated security attacks. As software becomes more intelligent, the security technologies used to attack and defend people from these applications must also leverage a similar level of intelligence.