SELEST: Secure elevation estimation of drones using MPC

Abstract

Drones are increasingly associated with incidents disturbing air traffic at airports, invading privacy, and even terrorism. Wireless Direction of Arrival (DoA) techniques, such as the MUSIC algorithm, can localize drones, but deploying a system that systematically localizes RF emissions can lead to intentional or unintentional (e.g., if compromised) abuse. Multi-Party Computation (MPC) provides a solution for controlled computation of the elevation of RF emissions, only revealing estimates when some conditions are met, such as when the elevation exceeds a specified threshold. However, we show that a straightforward implementation of MUSIC, which relies on costly computation of complex matrix operations such as eigendecomposition, in state of the art MPC frameworks is extremely inefficient requiring over 20 seconds to achieve the weakest security guarantees. In this work, we develop a set of MPC optimizations and extensions of MUSIC. We extensively evaluate our techniques in several MPC protocols achieving a speedup of 300-500 times depending on the security model and specific technique used. For instance a Malicious Shamir execution providing security against malicious adversaries enables 536 DoA estimations per second, making it practical for use in real-world setups.