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Laser Induced Deep Etching (LIDE®), developed by LPKF, is a maskless laser processing method capable of patterning glass microstructures similar to microfluidics created by PDMS soft lithography. Here, we demonstrate a self-digitized droplet microfluidics chip with high aspect-ratio features and fine resolution via the LIDE® technology. LIDE® provides the means to translate microfluidic designs into glass in a process suitable for low-cost and high-volume manufacturing.Free, publicly-accessible full text available December 1, 2023
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BioTag: Robust RFID-based Continuous User Verification Using Physiological Features from RespirationFree, publicly-accessible full text available October 17, 2023
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Free, publicly-accessible full text available July 1, 2023
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Free, publicly-accessible full text available June 27, 2023
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Local differential privacy (LDP) can be adopted to anonymize richer user data attributes that will be input to sophisticated machine learning (ML) tasks. However, today’s LDP approaches are largely task-agnostic and often lead to severe performance loss – they simply inject noise to all data attributes according to a given privacy budget, regardless of what features are most relevant for the ultimate task. In this paper, we address how to significantly improve the ultimate task performance with multi-dimensional user data by considering a task-aware privacy preservation problem. The key idea is to use an encoder-decoder framework to learn (and anonymize) a task-relevant latent representation of user data. We obtain an analytical near-optimal solution for the linear setting with mean-squared error (MSE) task loss. We also provide an approximate solution through a gradient-based learning algorithm for general nonlinear cases. Extensive experiments demonstrate that our task-aware approach significantly improves ultimate task accuracy compared to standard benchmark LDP approaches with the same level of privacy guarantee.