Results 41 to 50 of about 164,354 (261)

Local Differential Privacy for Belief Functions

Proceedings of the AAAI Conference on Artificial Intelligence, 2022
In this paper, we propose two new definitions of local differential privacy for belief functions. One is based on Shafer’s semantics of randomly coded messages and the other from the perspective of imprecise probabilities. We show that such basic properties as composition and post-processing also hold for our new definitions.
Li, Qiyu, Zhou, Chunlai, Qin, Biao, Xu, Zhiqiang   +3 more
openaire   +2 more sources

Survey on differential privacy and its progress

Tongxin xuebao, 2017
With the arrival of the era of big data sharing,data privacy protection issues will be highlighted.Since its introduction in 2006,differential privacy technology has been widely researched in data mining and data publishing.In recent years,Google,Apple ...
Zhi-qiang GAO, Yu-tao WANG
doaj   +2 more sources

Set-valued data collection with local differential privacy based on category hierarchy

Mathematical Biosciences and Engineering, 2021
Set-valued data is extremely important and widely used in sensor technology and application. Recently, privacy protection for set-valued data under differential privacy (DP) has become a research hotspot.
Jia Ouyang, Yinyin Xiao, Shaopeng Liu, Zhenghong Xiao, Xiuxiu Liao   +4 more
doaj   +1 more source

Convex Optimization for Linear Query Processing under Approximate Differential Privacy

, 2016
Differential privacy enables organizations to collect accurate aggregates over sensitive data with strong, rigorous guarantees on individuals' privacy. Previous work has found that under differential privacy, computing multiple correlated aggregates as a
Hao, Zhifeng, Yang, Yin, Yuan, Ganzhao, Zhang, Zhenjie   +3 more
core   +1 more source

Privacy-Preserving Distributed Learning via Newton Algorithm

Mathematics, 2023
Federated learning (FL) is a prominent distributed learning framework. The main barriers of FL include communication cost and privacy breaches. In this work, we propose a novel privacy-preserving second-order-based FL method, called GDP-LocalNewton.
Zilong Cao, Xiao Guo, Hai Zhang
doaj   +1 more source

The Role of Interactivity in Local Differential Privacy

, 2019
We study the power of interactivity in local differential privacy. First, we focus on the difference between fully interactive and sequentially interactive protocols.
Joseph, Matthew, Mao, Jieming, Neel, Seth, Roth, Aaron   +3 more
core   +1 more source

A trajectory data publishing algorithm satisfying local suppression

International Journal of Distributed Sensor Networks, 2021
Suppressing the trajectory data to be released can effectively reduce the risk of user privacy leakage. However, the global suppression of the data set to meet the traditional privacy model method reduces the availability of trajectory data.
Xiaohui Li, Yuliang Bai, Yajun Wang, Bo Li   +3 more
doaj   +1 more source

When and where do you want to hide? Recommendation of location privacy preferences with local differential privacy

, 2019
In recent years, it has become easy to obtain location information quite precisely. However, the acquisition of such information has risks such as individual identification and leakage of sensitive information, so it is necessary to protect the privacy ...
A Wasef, C Dwork, H Shin, L Sweeney, N Sadeh, SL Warner, Y Koren, YA Montjoye De, Z Huo   +8 more
core   +1 more source

Exercise Interventions in Children, Adolescents and Young Adults With Paediatric Bone Tumours—A Systematic Review

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Bone tumours present significant challenges for affected patients, as multimodal therapy often leads to prolonged physical limitations. This is particularly critical during childhood and adolescence, as it can negatively impact physiological development and psychosocial resilience.
Jennifer Queisser, Claire Laurie, Teresa Schmidt, Irene von Luettichau, Peter Bekkering, Sabine Kesting   +5 more
wiley   +1 more source

Triangle Counting With Local Edge Differential Privacy

Random Structures & Algorithms
ABSTRACTMany 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, Quanquan C. Liu, Sofya Raskhodnikova, Adam Smith   +3 more
openaire   +4 more sources