Results 71 to 80 of about 29,077 (283)

Marginal Release Under Local Differential Privacy [PDF]

Proceedings of the 2018 International Conference on Management of Data, 2018
Many analysis and machine learning tasks require the availability of marginal statistics on multidimensional datasets while providing strong privacy guarantees for the data subjects. Applications for these statistics range from finding correlations in the data to fitting sophisticated prediction models. In this paper, we provide a set of algorithms for
Graham Cormode, Tejas Kulkarni, Divesh Srivastava   +2 more
openaire   +2 more sources

Clustering Algorithm Reveals Dopamine‐Motor Mismatch in Cognitively Preserved Parkinson's Disease

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To explore the relationship between dopaminergic denervation and motor impairment in two de novo Parkinson's disease (PD) cohorts. Methods n = 249 PD patients from Parkinson's Progression Markers Initiative (PPMI) and n = 84 from an external clinical cohort.
Rachele Malito, Chiara Meneghini, Alice Galli, Luca Gallo, Pierfrancesco Mitrotti, Paola Dimartino, Marianna Inglese, Cinzia Zatti, Alessandro Lupini, Andrea Pilotto, Alessandro Padovani, Micol Avenali, Enza Maria Valente, Cristina Tassorelli, Silvia P. Caminiti   +14 more
wiley   +1 more source

The Privacy Funnel from the viewpoint of Local Differential Privacy

CoRR, 2020
To appear at: ICDS 2020, PPODS ...
openaire   +2 more sources

Location‐Specific Hematoma Volume Predicts Early Neurological Deterioration in Supratentorial ICH

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Early neurological deterioration (END) adversely affects outcomes in patients with intracerebral hemorrhage (ICH). This study aimed to determine the location‐specific hematoma volumes for END in supratentorial ICH patients. Methods We retrospectively analyzed supratentorial ICH patients presenting from two prospective cohorts.
Zuoqiao Li, Guilin Meng, Zijie Wang, Xiao Hu, Jing Cheng, Chu Chen, Tiannan Yang, Jiaxin Luo, Zizhen Meng, Xueyun Liu, Qi Li   +10 more
wiley   +1 more source

RBDPM: Risk-Based Differential Privacy Model for Trajectory Data [PDF]

Personal safety applications enable users to communicate emergency situations to relevant third parties and local authorities. Location-Based Services play a crucial role in the capture and exchange of data, including location and personal identifiable ...
Alofe, O.
core   +1 more source

Effectiveness of rTMS on Working Memory and Inhibitory Impairments in Patients With Post‐Stroke Executive Deficits

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Considerable efforts have been dedicated to developing effective treatments for post‐stroke executive impairment (PSEI), among which repetitive transcranial magnetic stimulation (rTMS) has shown great potential. This study aimed to investigate the therapeutic effects of high‐frequency rTMS on working memory (WM) and response ...
Mengting Lao, Rongwei Du, Zhujun Wang, Yang Gao, Meiling Cheng, Xianglong Wang, Wen Wu   +6 more
wiley   +1 more source

Triangle Counting with Local Edge Differential Privacy [PDF]

, 2023
Many 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 noninteractive and interactive local model with edge
Raskhodnikova, Sofya, Smith, Adam, Eden, Talya, Liu, Quanquan C.   +3 more
core   +1 more source

Predictive Ability of Plasma p‐tau217 for β‐Amyloid Status: A Prospective Multicenter Study

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Plasma tau phosphorylated at threonine 217 (p‐tau217) measured with fully automated platforms has shown high accuracy for Alzheimer's disease (AD) diagnosis, but real‐world multicenter data remain limited. We aimed to validate the diagnostic performance of p‐tau217 for identifying AD pathology in a real‐world multicenter cohort ...
Miquel Massons, Nuria Guillen, Jordi Sarto, Neus Falgàs, Sergi Borrego‐Écija, Diana Esteller‐Gauxax, Magda Castellví, Adrià Tort‐Merino, Agnès Pérez‐Millan, Anna Antonell, Josep M. Augè Fradera, Gerard Piñol, Iolanda Riba, Anna Carnes‐Vendrell, Marta Cullell, Maria Teresa Osuna, Lorena Bajo, Teresa Romero, Eva Bonjoch, Joan Bello, Susana Fernández, Marta Balagué, Isabel Gómez‐Ruiz, Anuncia Boltes, Claustre Pont, Raquel Cuevas, Sara Carrillo, Laura Iglesias, Teresa Maria Casadevall Codina, Lorena Grau Guinea, Fernando Jose Espada, Raquel Sánchez‐Valle, Mircea Balasa, Albert Lladó   +33 more
wiley   +1 more source

Study on utility optimization for randomized response mechanism

Tongxin xuebao, 2019
For the study of privacy-utility trade-off in local differential privacy,the utility optimization models of binary generalized random response mechanism for the case of differential privacy and approximate differential privacy were established.By graphic
Yihui ZHOU, Laifeng LU, Zhenqiang WU
doaj   +2 more sources

Local and Central Differential Privacy for Robustness and Privacy in Federated Learning

Proceedings 2022 Network and Distributed System Security Symposium, 2022
Federated Learning (FL) allows multiple participants to train machine learning models collaboratively by keeping their datasets local while only exchanging model updates. Alas, this is not necessarily free from privacy and robustness vulnerabilities, e.g., via membership, property, and backdoor attacks.
Mohammad Naseri, Jamie Hayes, Emiliano De Cristofaro   +2 more
openaire   +3 more sources