Results 121 to 130 of about 7,877,596 (288)

LDPart: Effective Location-Record Data Publication via Local Differential Privacy

open access: yesIEEE Access, 2019
Driven by the advance of positioning technology and the tremendous popularity of location-based services, location-record data have become unprecedentedly available.
Xiangguo Zhao   +4 more
doaj   +1 more source

Wearable Smart Patches on Sustainable Chitosan With Reversible Microbial Growth‐Inhibitory Properties

open access: yesAdvanced Functional Materials, EarlyView.
ABSTRACT Chitosan, a polysaccharide upcycled from biowaste, has long promised sustainable, biocompatible, and antimicrobial films and devices, an appeal reinforced by its recent regulatory recognition, with several chitosan‐based antibacterial dressings gaining clearance through the Food and Drug Administration's 510(k) pathway.
Jacopo Nicoletti   +16 more
wiley   +1 more source

Triangle Counting With Local Edge Differential Privacy

open access: yesRandom Structures & Algorithms
ABSTRACTMany deployments of differential privacy in industry are in the local model, where each party releases its private information via a differentially private randomizer. We study triangle counting in the non‐interactive and interactive local model with edge differential privacy (that, intuitively, requires that the outputs of the algorithm on ...
Talya Eden   +3 more
openaire   +6 more sources

Electrospun Wood‐Derived Biopolymers as Electrodes in Electrochemical Energy Storage Technologies

open access: yesAdvanced Functional Materials, EarlyView.
Electrospinning transforms wood‐derived cellulose and lignin into architecturally defined, binder‐free carbon electrodes with tuneable porosity and functionality. This review shows how fibre design enables decoupled charge and mass transport, enhancing performance across battery systems, while identifying key challenges in spinnability, scalability ...
Michael W. Thielke   +3 more
wiley   +1 more source

Metric Differential Privacy on the Special Orthogonal Group SO(3)

open access: yes
Differential privacy (DP) is an important framework to provide strong theoretical guarantees on the privacy and utility of released data. Since its introduction in 2006, DP has been applied to various data types and domains.
Anna Katharina Hildebrandt   +2 more
core   +1 more source

Heterogeneous Differential Privacy

open access: yes, 2017
The massive collection of personal data by personalization systems has rendered the preservation of privacy of individuals more and more difficult. Most of the proposed approaches to preserve privacy in personalization systems usually address this issue ...
Sébastien Gambs   +2 more
core   +1 more source

Physics‐Grounded Materials Artificial Intelligence for Reliable Materials Discovery

open access: yesAdvanced Functional Materials, EarlyView.
Physics‐Grounded Materials AI (PhysMat AI) integrates physical priors, descriptors, constraints, verification, and data infrastructure into a unified full‐stack framework, enabling reliable, interpretable, and autonomous AI‐driven materials discovery.
Yuhang Wang   +3 more
wiley   +1 more source

Sampling and Partitioning for Differential Privacy

open access: yes, 2016
Differential privacy enjoys increasing popularity thanks to both a precise semantics for privacy and effective enforcement mechanisms. Many tools have been proposed to spread its use and ease the task of the concerned data scientist.
David Sands   +9 more
core   +1 more source

Genetically Programmed Shape‐Morphing of Engineered Living Materials

open access: yesAdvanced Functional Materials, EarlyView.
Here, bacterial and mammalian engineered living materials (ELMs) are presented, in which living cells direct genetically programmed and enzymatically mediated shape‐morphing. The resulting deformation and shape‐recovery can be actuated in a genetically controlled manner by an ELM incorporating out‐of‐equilibrium counteracting biochemical reactions with
Jan Becker   +7 more
wiley   +1 more source

Evaluation of auditing mechanisms for differential privacy

open access: yes
openLa Differential Privacy (DP) è una tecnica fondamentale per garantire la riservatezza dei dati sensibili, introducendo rumore controllato nei risultati delle analisi.
PJETRI, ERIK
core  

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