Results 51 to 60 of about 58,983 (255)
ABSTRACT Background Maintenance hemodialysis (MHD) patients frequently suffer from frailty, characterized by reduced physical function and poor prognosis. Myokines, such as myonectin, secreted by muscle, are emerging regulators of systemic health. This study investigated the relationship between serum myonectin, adipokines (adiponectin, omentin), and ...
Kenichi Kono +7 more
wiley +1 more source
Computational Differential Privacy [PDF]
The definition of differential privacy has recently emerged as a leading standard of privacy guarantees for algorithms on statistical databases. We offer several relaxations of the definition which require privacy guarantees to hold only against efficient--i.e., computationally-bounded--adversaries.
Ilya Mironov +3 more
openaire +1 more source
Local differential privacy (LDP), where users randomly perturb their inputs to provide plausible deniability of their data without the need for a trusted party, has been adopted recently by several major technology organizations, including Google, Apple and Microsoft.
Graham Cormode +5 more
openaire +2 more sources
ABSTRACT Background Chronic micro‐inflammation in patients with end‐stage renal disease (ESRD) is a significant driver of cardiovascular complications and diminished quality of life. While standard hemodialysis (SHD) effectively manages small‐molecule clearance, its ability to remove medium‐to‐large uremic toxins—the primary catalysts of systemic ...
Hongwei Zuo +5 more
wiley +1 more source
A Survey of Differential Privacy Techniques for Federated Learning
The problem of data privacy protection in the information age deserves people’s attention. As a distributed machine learning technology, federated learning can effectively solve the problem of privacy security and data silos.
Wang Xin +4 more
doaj +1 more source
Age-Dependent Differential Privacy
The proliferation of real-time applications has motivated extensive research on analyzing and optimizing data freshness in the context of age of information. However, classical frameworks of privacy (e.g., differential privacy (DP)) have overlooked the impact of data freshness on privacy guarantees, and hence may lead to unnecessary accuracy loss when ...
Meng Zhang 0013 +3 more
openaire +3 more sources
In normal (nontolerant) cells, CD14 is crucial for both LPS uptake and LPS signaling. In LPS‐tolerant cells, in which LPS‐induced TNF‐α and IFN‐β production is suppressed, there is a dramatic increase in surface CD14 expression. The overexpressed CD14 in LPS‐tolerant cells is responsible for the enhanced LPS uptake without inducing pro‐inflammatory ...
Saeka Nishihara +3 more
wiley +1 more source
This systematic review synthesizes prognostic models for survival and recurrence in resected non‐small cell lung cancer. While many models demonstrate moderate to good discrimination, few are externally validated and reporting quality is variable, limiting clinical applicability and highlighting the need for robust, transparent model development ...
Evangeline Samuel +4 more
wiley +1 more source
Differential Privacy in Practice
We briefly review the problem of statistical disclosure control under differential privacy model, which entails a formal and ad omnia privacy guarantee separating the utility of the database and the risk due to individual participation. It has born fruitful results over the past ten years, both in theoretical connections to other fields and in ...
Huu HiepNguyen, Kim, J, Yoonho Kim
openaire +3 more sources
Federated $f$-Differential Privacy
Federated learning (FL) is a training paradigm where the clients collaboratively learn models by repeatedly sharing information without compromising much on the privacy of their local sensitive data. In this paper, we introduce federated $f$-differential privacy, a new notion specifically tailored to the federated setting, based on the framework of ...
Qinqing Zheng +3 more
openaire +4 more sources

