Results 51 to 60 of about 164,354 (261)
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
Bridging the gap: Multi‐stakeholder perspectives of molecular diagnostics in oncology
Molecular Oncology, EarlyView.Although molecular diagnostics is transforming cancer care, implementing novel technologies remains challenging. This study identifies unmet needs and technology requirements through a two‐step stakeholder involvement. Liquid biopsies for monitoring applications and predictive biomarker testing emerge as key unmet needs. Technology requirements vary by
Jorine Arnouts +8 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.This study characterizes the responses of primary acute myeloid leukemia (AML) patient samples to the MCL‐1 inhibitor MIK665. The results revealed that monocytic differentiation is associated with MIK665 sensitivity. Conversely, elevated ABCB1 expression is a potential biomarker of resistance to the treatment, which can be overcome by the combination ...
Joseph Saad +17 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
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
Privacy Enhanced Matrix Factorization for Recommendation with Local Differential Privacy
IEEE Transactions on Knowledge and Data Engineering, 2018 Recommender systems are collecting and analyzing user data to provide better user experience. However, several privacy concerns have been raised when a recommender knows user's set of items or their ratings. A number of solutions have been suggested to improve privacy of legacy recommender systems, but the existing solutions in the literature can ...
Hyejin Shin +3 more
openaire +3 more sources
Molecular Oncology, EarlyView.Liquid biopsy enables minimally invasive, real‐time molecular profiling through analysis of circulating biomarkers in biological fluids. This Perspective highlights the importance of training pathologists through integrative educational programs, such as the European Masters in Molecular Pathology, to ensure effective and equitable implementation of ...
Marius Ilié +13 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