Transitioning from testbeds to ships: an experience study in deploying the TIPPERS Internet of Things platform to the US Navy. (July 2022)

• Record Type: Journal Article
• Authors: Archer, Dave; August, Michael A; Bouloukakis, Georgios; Davison, Christopher; Diallo, Mamadou H; Ghosh, Dhrubajyoti; Graves, Christopher T; Hay, Michael; He, Xi; Laud, Peeter; Lu, Steve; Machanavajjhala, Ashwin; Mehrotra, Sharad; Miklau, Gerome; Pankova, Alisa; Sharma, Shantanu; Venkatasubramanian, Nalini; Wang, Guoxi; Yus, Roberto
• Is Part Of: Journal of defense modeling and simulation. Volume 19:Number 3(2022)
• Page Start: 501
• Page End: 517
• Publication Date: 2022-07
• Subjects: Internet of Things -- privacy -- US Navy -- deployment -- practical experiences -- secure computing -- differential privacy -- data management -- middleware; Military art and science -- Computer simulation -- Periodicals; 355.0011305
• DOI: 10.1177/1548512920956383
• Languages: English
• ISSNs: 1548-5129

Abstract

This paper describes the collaborative effort between privacy and security researchers at nine different institutions along with researchers at the Naval Information Warfare Center to deploy, test, and demonstrate privacy-preserving technologies in creating sensor-based awareness using the Internet of Things (IoT) aboard naval vessels in the context of the US Navy's Trident Warrior 2019 exercise. Funded by DARPA through the Brandeis program, the team built an integrated IoT data management middleware, entitled TIPPERS, that supports privacy by design and integrates a variety of Privacy Enhancing Technologies (PETs), including differential privacy, computation on encrypted data, and fine-grained policies. We describe the architecture of TIPPERS and its use in creating a smart ship that offers IoT-enabled services such as occupancy analysis, fall detection, detection of unauthorized access to spaces, and other situational awareness scenarios. We describe the privacy implications of creating IoT spaces that collect data that might include individuals' data (e.g., location) and analyze the tradeoff between privacy and utility of the supported PETs in this context.