We show that low resolution and fixed point nature of ultra-low-power implementations prevent privacy guarantees from being provided due to low quality noising.
In this work, we explore the feasibility of providing local differential privacy (LDP) on ultra-low-power (ULP) systems that power many sensor and IoT system ...
To bypass the compute overheads associated with software algorithms, Choi et al. [27] proposed a hardwarelevel local differential privacy implementation by ...
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Guaranteeing local differential privacy on ultra-low-power systems
dl.acm.org › doi › ISCA.2018.00053
Jun 2, 2018 · In this work, we explore the feasibility of providing local differential privacy on ultra-low-power systems that power many sensor and IoT ...
Feb 8, 2023 · One approach is given by the recent work [Choi et al. “Guaranteeing Local Differential Privacy on Ultra-Low-Power Systems” 2018 ACM/IEEE 45th ...
PD-OPF satisfies the notion of local differential privacy, providing strong privacy guarantees. (2) Experimental results on a large collection of OPF benchmarks ...
Dec 22, 2020 · [24] explore the feasibility of applying LDP on ultra-low-power (ULP) systems. They use resampling, thresholding, and a privacy budget control.
Duration: 2:08
Posted: May 13, 2018
Posted: May 13, 2018
Dec 11, 2023 · This publication describes differential privacy — a mathematical framework that quantifies privacy risk to individuals as a consequence of ...
Missing: Ultra- | Show results with:Ultra-
In general, local differential privacy provides a strong privacy guarantee to statistical calculations allowing users to keep their data on their own ...