Results 121 to 130 of about 64,907 (298)

How differential privacy will affect our understanding of population growth in the United States

open access: yes, 2020
The implementation of a proposed differential privacy algorithm to 2020 US Census data releases, and other census products has brought about discussions about the consistency and reliability of the data produced under the proposed disclosure avoidance ...
Danilo T Perez-Rivera   +2 more
core   +1 more source

Comprehensive Assessment of Arterial, Tissue, and Venous Collaterals for Evaluating the Infarct Growth Rate: The Multimodal Collateral Score

open access: yesAnnals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Background Collaterals are crucial factors that influence the infarct growth rate (IGR). We aimed to determine whether a comprehensive multimodal collateral score (MCS), incorporating collateral assessment at the arterial, tissue, and venous levels, is associated with functional independence and provides incremental prognostic value over ...
Giorgio Busto   +12 more
wiley   +1 more source

Replication Package for "A Practical Method to Reduce Privacy Loss when Disclosing Statistics Based on Small Samples"

open access: yes, 2019
We develop a simple method to reduce privacy loss when disclosing statistics such as OLS regression estimates based on samples with small numbers of observations.
Chetty, Raj (8343501)   +3 more
core   +1 more source

Risk of Non‐Arteritic Anterior Ischemic Optic Neuropathy in Idiopathic Intracranial Hypertension Patients Treated with GLP‐1 Receptor Agonists

open access: yesAnnals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Introduction Glucagon‐like peptide‐1 receptor agonists (GLP‐1 RAs) have demonstrated significant weight‐reducing effects and may offer benefits in idiopathic intracranial hypertension (IIH); however, recent concerns about the risk of non‐arteritic anterior ischemic optic neuropathy (NAION) have emerged.
Faisal A. Al‐Harbi   +9 more
wiley   +1 more source

Computational Differential Privacy for Encrypted Databases Supporting Linear Queries

open access: yes
International audienceDifferential privacy is a fundamental concept for protecting individual privacy in databases while enabling data analysis.
Alborch Escobar, Ferran   +3 more
core   +1 more source

Sertraline Treatment Can Mimic Niemann‐Pick Type C Biomarker Profile: A Diagnostic Pitfall

open access: yesAnnals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Background Oxysterols (cholestane‐3β,5α,6β‐triol and 7‐ketocholesterol) and N‐palmitoyl‐O‐phosphocholineserine (PPCS) are sensitive biomarkers for Niemann‐Pick disease type C (NPC) screening. However, false‐positive results occur, with a biomarker profile suggestive of NPC despite the absence of pathogenic variants in genes involved in NPC or ...
Maria Makrygianni   +19 more
wiley   +1 more source

DPRF: A Differential Privacy Protection Random Forest

open access: yesIEEE Access, 2019
Providing privacy protection for classification algorithms has become a research hotspot in current data mining. In this paper, differential privacy is applied to the random forest classification algorithm, and a random forest algorithm based on ...
Jun Hou   +5 more
doaj   +1 more source

Comparative Effectiveness and Safety of Inebilizumab Versus Rituximab in AQP4‐IgG‐Positive NMOSD

open access: yesAnnals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Rituximab (anti‐CD20, RTX) and inebilizumab (anti‐CD19, INE) represent B‐cell‐depleting therapies used for aquaporin‐4 antibody‐positive (AQP4‐IgG+) neuromyelitis optica spectrum disorder (NMOSD); however, direct comparative evidence remains limited.
Jie Lin   +11 more
wiley   +1 more source

Differential privacy for collaborative filtering recommender algorithm

open access: yes, 2016
Collaborative filtering plays an essential role in a recommender system, which recommends a list of items to a user by learning behavior patterns from user rating matrix. However, if an attacker has some auxiliary knowledge about a user purchase history,
Xue Zhu, Yuqing Sun, Sun, Y, Zhu, X
core   +1 more source

Wasserstein Differential Privacy

open access: yesProceedings of the AAAI Conference on Artificial Intelligence
Differential privacy (DP) has achieved remarkable results in the field of privacy-preserving machine learning. However, existing DP frameworks do not satisfy all the conditions for becoming metrics, which prevents them from deriving better basic private properties and leads to exaggerated values on privacy budgets.
Chengyi Yang, Jiayin Qi, Aimin Zhou
openaire   +2 more sources

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