Special Issue on Privacy-preserving Representation Learning for Big Data (PRLBD)
摘要截稿:
全文截稿: 2019-05-31
影响因子: 4.438
期刊难度:
CCF分类: C类
中科院JCR分区:
• 大类 : 计算机科学 - 2区
• 小类 : 计算机:人工智能 - 2区
Overview
Big data needs huge storage space, and its applications require powerful computation capability. Recently, it is very popular for data owners to outsource big data from local servers to cloud due to the great flexibility and cost saving of cloud computing, such as managing the massive computation workload in representation learning and data retrieval. However, representation learning on cloud data may reveal privacy of data owners, such as personal identity, location, and financial profiles hobbies. To do this, data owners can encrypt their data for confidentiality before uploading them to cloud. However, encrypted data makes its feature extraction (i.e., representation learning) difficult. The attacker can deduce the data content via feature comparisons in benchmark data sets, or even recover part of the data based on the derived features. Thus, exploring privacy-preserving representation learning from big data becomes of very importance in the domains of both machine learning and data cybersecurity.
In this special issue, we invite papers to address many challenges of representation learning from big data. EspeciallySpecifically, to provide readers of the this special issue with a state-of-the-art background on the topic, we will invite one survey paper, which will undergo the peer review process. The list of possible topics include, but not limited to:
Privacy-preserving representation learning for big multimedia data
Data preprocess including missing value imputation, feature selection , clustering, and synthesizing/fusion
Distributed/paralleled/sampling techniques for big data
Sparse/Dictionary learning from big data
Privacy-preserving representation learning or feature extraction
Privacy-preserving deep learning
Privacy-preserving deep learning framework
Secured kNN algorithm on encrypted data
Secured deep hashing techniques for image retrieval
Secured representation learning methods using deep learning models
Privacy-preserving representation learning for multi-task data
Secured similarities/dissimilarities learning from multiple tasks
Secured regularization strategies in multi-task learning
Large tasks (modals), small sample size learning for secured multi-task learning, domain adaptation, and transfer learning