Results 51 to 60 of about 166,795 (258)
Triangle Counting With Local Edge Differential Privacy
Random Structures & AlgorithmsABSTRACTMany 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 +4 more sources
Classification under local differential privacy
, 2019 We consider the binary classification problem in a setup that preserves the privacy of the original sample. We provide a privacy mechanism that is locally differentially private and then construct a classifier based on the private sample that is universally consistent in Euclidean spaces.
Berrett, Thomas, Butucea, Cristina
openaire +2 more sources
Molecular Oncology, EarlyView.Development of therapies targeting cancer‐associated fibroblasts (CAFs) necessitates preclinical model systems that faithfully represent CAF–tumor biology. We established an in vitro coculture system of patient‐derived pancreatic CAFs and tumor cell lines and demonstrated its recapitulation of primary CAF–tumor biology with single‐cell transcriptomics ...
Elysia Saputra +10 more
wiley +1 more source
Distributed Private Heavy Hitters
, 2012 In this paper, we give efficient algorithms and lower bounds for solving the heavy hitters problem while preserving differential privacy in the fully distributed local model.
A. Beimel +4 more
core +2 more sources
The Privacy-Utility Tradeoff of Robust Local Differential Privacy
, 2021 We consider data release protocols for data $X=(S,U)$, where $S$ is sensitive; the released data $Y$ contains as much information about $X$ as possible, measured as $\operatorname{I}(X;Y)$, without leaking too much about $S$. We introduce the Robust Local Differential Privacy (RLDP) framework to measure privacy.
Lopuhaä-Zwakenberg, Milan +1 more
openaire +3 more sources
Molecular Oncology, EarlyView.Translational cancer research and its implementation through competitively selected Comprehensive Cancer Centers across Europe should be the primary policy focus for addressing the increasing cancer burden in Europe and counteract the present main strategy to convert cancer to a chronic disease.
Manuel Heitor +2 more
wiley +1 more source
Context-Aware Local Differential Privacy
, 2019 Local differential privacy (LDP) is a strong notion of privacy for individual users that often comes at the expense of a significant drop in utility. The classical definition of LDP assumes that all elements in the data domain are equally sensitive. However, in many applications, some symbols are more sensitive than others. This work proposes a context-
Acharya, Jayadev +4 more
openaire +2 more sources
Aging and Cancer, EarlyView.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
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective People with epilepsy (PWE) may experience cognitive deficits but fail to undergo formal evaluation. This study compares cognitive status between PWE and healthy controls in the West African Republic of Guinea. Methods A cross‐sectional, case–control study was conducted in sequential recruitment phases (July 2024–July 2025) at Ignace ...
Maya L. Mastick +14 more
wiley +1 more source
Robust Optimization for Local Differential Privacy
2022 IEEE International Symposium on Information Theory (ISIT), 2022 We consider the setting of publishing data without leaking sensitive information. We do so in the framework of Robust Local Differential Privacy (RLDP). This ensures privacy for all distributions of the data in an uncertainty set. We formulate the problem of finding the optimal data release protocol as a robust optimization problem.
Goseling, Jasper +1 more
openaire +4 more sources