Privacy in Urban Sensing with Instrumented Fleets, Using Air Pollution Monitoring As A Usecase

Abidi, Ismi; Nangia, Ishan; Aditya, Paarijaat; Sen, Rijurekha

doi:10.14722/ndss.2022.23127

Cited by 3 publications

(4 citation statements)

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“…In our prototype, we use the ARM TrustZone [10], which provides these features. 3 An ARM TrustZone processor can be in one of two worlds of execution at any given instant-a secure world or a normal world. The secure world, also called the trusted-execution environment (or the TEE), runs trusted software services.…”

Section: Auditing Compliance With Pd-rosmentioning

confidence: 99%

“…Abidi et al [3] consider a setting in which citizens query aggregate pollution statistics collected from sensors fitted on taxi fleets. As in Privadome, they protect citizen privacy using MPC.…”

Section: Related Workmentioning

confidence: 99%

“…This is a weaker privacy guarantee than the one provided by Algorithm 2, which runs the computation on all drones. For example, it may be possible for the regulatory authority to use live drone traffic density maps to estimate the approximate locations from where the citizen issues queries, which may be unacceptable in some settings (e.g., see Abidi et al [3] for such an attack).…”

Section: Appendix A1 Pd-mpc Using Garbled Circuitsmentioning

confidence: 99%

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Privadome: Delivery Drones and Citizen Privacy

Pillai,

Suresh,

Gupta

et al. 2024

PoPETs

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E-commerce companies are actively considering the use of delivery drones for customer fulfillment, leading to growing concerns around citizen privacy. Drones are equipped with cameras, and the video feed from these cameras is often required as part of routine navigation, be it for semi-autonomous or fully-autonomous drones. Footage of ground-based citizens captured in these videos may lead to privacy concerns. This paper presents Privadome, a system that implements the vision of a virtual privacy dome centered around the citizen. Privadome is designed to be integrated with city-scale regulatory authorities that oversee delivery drone operations and realizes this vision through two components, PD-MPC and PD-ROS. PD-MPC allows citizens equipped with a mobile device to identify drones that have captured their footage. It uses secure two-party computation to achieve this goal without compromising the privacy of the citizen’s location. PD-ROS allows the citizen to communicate with such drones and obtain an audit trail showing how the drone uses their footage and determine if privacy-preserving steps are taken to sanitize the footage.

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Section: Auditing Compliance With Pd-rosmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Appendix A1 Pd-mpc Using Garbled Circuitsmentioning

confidence: 99%

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Privadome: Delivery Drones and Citizen Privacy

Pillai,

Suresh,

Gupta

et al. 2024

PoPETs

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Section: Codementioning

confidence: 99%

Privadome: Protecting Citizen Privacy from Delivery Drones

Pillai¹,

Gupta²,

Suresh³

et al. 2022

Preprint

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As e-commerce companies begin to consider using delivery drones for customer fulfillment, there are growing concerns around citizen privacy. Drones are equipped with cameras, and the video feed from these cameras is often required as part of routine navigation, be it for semiautonomous or fully-autonomous drones. Footage of ground-based citizens may be captured in this video feed, thereby leading to privacy concerns.This paper presents Privadome, a system that implements the vision of a virtual privacy dome centered around the citizen. Privadome is designed to be integrated with city-scale regulatory authorities that oversee delivery drone operations and realizes this vision through two components, PD-MPC and PD-ROS. PD-MPC allows citizens equipped with a mobile device to identify drones that have captured their footage. It uses secure two-party computation to achieve this goal without compromising the privacy of the citizen's location. PD-ROS allows the citizen to communicate with such drones and obtain an audit trail showing how the drone uses their footage and determine if privacy-preserving steps are taken to sanitize the footage. An experimental evaluation of Privadome using our prototype implementations of PD-MPC and PD-ROS shows that the system scales to near-term city-scale delivery drone deployments (hundreds of drones). We show that with PD-MPC the mobile data usage on the citizen's mobile device is comparable to that of routine activities on the device, such as streaming videos. We also show that the workflow of PD-ROS consumes a modest amount of additional CPU resources and power on our experimental platform.

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