Privaros: A Framework for Privacy-Compliant Delivery Drones

Beck, Rakesh Rajan; Vijeev, Abhishek; Ganapathy, Vinod

doi:10.1145/3372297.3417858

Cited by 15 publications

(17 citation statements)

References 46 publications

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“…The current state of mobile/IoT security threats is too big for a general-purpose solution. As a result, recently, we have observed a surge in domain-specific solutions for improving security and privacy on mobile/IoT use cases [63,37,13,24,21]. To name a few, Privaros [13] enforces host-specified privacy policies on delivery drones, while McReynolds et al tackle privacy issues on children's smart toys [43], and Aggio [24] focuses on privacy-preserving smart retail environments.…”

Section: Related Workmentioning

confidence: 99%

“…As a result, recently, we have observed a surge in domain-specific solutions for improving security and privacy on mobile/IoT use cases [63,37,13,24,21]. To name a few, Privaros [13] enforces host-specified privacy policies on delivery drones, while McReynolds et al tackle privacy issues on children's smart toys [43], and Aggio [24] focuses on privacy-preserving smart retail environments. Additionally, MegaMind [63] offers a solution targeted to voice assistants, SpecEye [38] proposes a privacy-preserving screen exposure detection system, and SecureSIM [73] provides an access control specific to in-SIM files.…”

Section: Related Workmentioning

confidence: 99%

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Enhancing the Security & Privacy of Wearable Brain-Computer Interfaces

Tarkhani¹,

Qendro²,

Brown³

et al. 2022

Preprint

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Brain computing interfaces (BCI) are used in a plethora of safety/privacy-critical applications, ranging from healthcare to smart communication and control. Wearable BCI setups typically involve a head-mounted sensor connected to a mobile device, combined with MLbased data processing. Consequently, they are susceptible to a multiplicity of attacks across the hardware, software, and networking stacks used that can leak users' brainwave data or at worst relinquish control of BCI-assisted devices to remote attackers.In this paper, we: (i) analyse the whole-system security and privacy threats to existing wearable BCI products from an operating system and adversarial machine learning perspective; and (ii) introduce Argus, the first information flow control system for wearable BCI applications that mitigates these attacks. Argus' domainspecific design leads to a lightweight implementation on Linux ARM platforms suitable for existing BCI usecases. Our proof of concept attacks on real-world BCI devices (Muse, NeuroSky, and OpenBCI) led us to discover more than 300 vulnerabilities across the stacks of six major attack vectors. Our evaluation shows Argus is highly effective in tracking sensitive dataflows and restricting these attacks with an acceptable memory and performance overhead (< 15%).

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Enhancing the Security & Privacy of Wearable Brain-Computer Interfaces

Tarkhani¹,

Qendro²,

Brown³

et al. 2022

Preprint

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“…Or it could mean that sensitive objects, such as faces and car registration plates identified in the video feed, are identified and blurred (i.e., pixelated). This notion has been used in prior work [4,14], and is also the approach that is employed to preserve citizen privacy in Google Street View. In fact, prior work has even developed (MPC-based) methods to allow individual citizens to specify their own privacy policies, e.g., to have just their appearance blurred or edited out of the footage altogether [4].…”

Section: Goals and Design Space Explorationmentioning

confidence: 99%

“…We also assume that SROS completely mediates application communication within the normal world. A malicious ROS2 application can attempt to bypass SROS by directly invoking OS abstractions, e.g., via raw sockets, to communicate with a colluding application [14]. Prior work has attempted to harden the normal world kernel against zero-day exploits [12,42], and to ensure that applications only communicate under the purview of SROS [14,7].…”

Section: Core Components Of Pd-rosmentioning

confidence: 99%

“…While Wi-Fly and Nassi et al's work focuses on how individual citizens are impacted, Privaros [14] and AliDrone [53] develop methods to regulate delivery drones over well-demarcated host airspaces that may dictate that specific policies are to be obeyed within the airspace. For example, a college campus or an apartment complex may require the delivery drone to ensure that images and video recorded in the their airspace be blurred suitably, or that drones follow certain pre-identified drone lanes during their visits.…”

Section: Related Workmentioning

confidence: 99%

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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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