Privacy in Information-Rich Intelligent Infrastructure
By Cynthia Dwork, George Pappas. 2017
12 citations recorded by the source corpus.
Intelligent infrastructure will critically rely on the dense instrumentation of sensors and actuators that constantly transmit streaming data to cloud-based analytics for real-time monitoring. For example, driverless cars communicate real-time location and other data to companies like Google, which aggregate regional data in order to provide real-time traffic maps. Such traffic maps can be extremely useful to the driver (for optimal travel routing), as well as to city transportation administrators for real-time accident response that can have an impact on traffic capacity. Intelligent infrastructure monitoring compromises the privacy of drivers who continuously share their location to cloud aggregators, with unpredictable consequences. Without a framework for protecting the privacy of the driver's data, drivers may be very conservative about sharing their data with cloud-based analytics that will be responsible for adding the intelligence to intelligent infrastructure. In the energy sector, the Smart Grid revolution relies critically on real-time metering of energy supply and demand with very high granularity. This is turn enables real-time demand response and creates a new energy market that can incorporate unpredictable renewable energy sources while ensuring grid stability and reliability. However, real-time streaming data captured by smart meters contain a lot of private information, such as our home activities or lack of, which can be easily inferred by anyone that has access to the smart meter data, resulting not only in loss of privacy but potentially also putting us at risk.
Subfields: Artificial Intelligence, Control and Systems Engineering, Information Systems
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Aleksandra Korolova
- Vasyl Pihur
- M. Yung
- Frederick Liu
- Dachuan Huang
- Subhash Sankuratripati
- Ruogu Zeng
- Xiaolong Bai
In Information Systems
Citations in the corpus
- Differentially-Private "Draw and Discard" Machine Learning (2018, background): “A major struggle in the application of DP to practice [25], [49] is the question of how to keep the overall per-user privacy loss [41], [14] bounded over time.”
- Privacy Loss in Apple's Implementation of Differential Privacy on MacOS 10.12 (2017, background): “Both EFF and academics have called for Apple to detail its privacy budget use [3, 8, 16, 20], to no avail1.”