Results 31 to 40 of about 166,795 (258)
Local Differential Privacy Graph Data Modeling Method for Link Prediction
Journal of Harbin University of Science and Technology, 2023 To solve the problem of node sensitive link privacy being exposed in the process of link prediction on industrial business graph data , according to the theory of local differential privacy , the shortcomings of the existing graph privacy protection ...
HANQilong, WUXiaoming
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Answering range queries under local differential privacy [PDF]
Proceedings of the VLDB Endowment, 2019 Counting the fraction of a population having an input within a specified interval i.e. a range query, is a fundamental data analysis primitive. Range queries can also be used to compute other core statistics such as quantiles, and to build prediction models.
Cormode, Graham +2 more
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Frequency estimation under local differential privacy [PDF]
Proceedings of the VLDB Endowment, 2021 Private collection of statistics from a large distributed population is an important problem, and has led to large scale deployments from several leading technology companies. The dominant approach requires each user to randomly perturb their input, leading to guarantees in the local differential privacy model.
Cormode, Graham +2 more
openaire +2 more sources
Multi-level local differential privacy algorithm recommendation framework
Tongxin xuebao, 2022 Local differential privacy (LDP) algorithm usually assigned the same protection mechanism and parameters to different users.However, it ignored the differences among the device resources and the privacy requirements of different users.For this reason, a ...
Hanyi WANG +5 more
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Behavior Sequence Mining Model Based on Local Differential Privacy
IEEE Access, 2020 Most of local differential privacy frameworks target statistics on certain privacy behaviors of users, but not behavior sequence. In this paper, we explore and propose a behavior sequence mining model that satisfies the local differential privacy ...
Jianen Yan, Yan Wang, Wenling Li
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Linear and Range Counting under Metric-based Local Differential Privacy
, 2020 Local differential privacy (LDP) enables private data sharing and analytics without the need for a trusted data collector. Error-optimal primitives (for, e.g., estimating means and item frequencies) under LDP have been well studied.
Ding, Bolin +3 more
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High-dimensional Data Publication Under Local Differential Privacy [PDF]
Jisuanji kexueWith the increasing availability of high-dimensional data collected from numerous users,preserving user privacy while utilizing high-dimensional data poses significant challenges.This paper focuses on the problem of high-dimensional data publication ...
CAI Mengnan, SHEN Guohua, HUANG Zhiqiu, YANG Yang
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K-Means Clustering with Local Distance Privacy
Big Data Mining and Analytics, 2023 With the development of information technology, a mass of data are generated every day. Collecting and analysing these data help service providers improve their services and gain an advantage in the fierce market competition.
Mengmeng Yang +2 more
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LDPORR: A localized location privacy protection method based on optimized random response
Journal of King Saud University: Computer and Information Sciences, 2023 The broad use of mobile intelligent terminals with locating functions encourages the rapid development of location-based services (LBS), which are widely used in a variety of industries such as social networking, transportation, finance, and ...
Yan Yan +4 more
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 +3 more
core +1 more source