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An enhanced MinHash encryption scheme for encrypted deduplication

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An enhanced MinHash encryption scheme for encrypted deduplic...

IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Qiaowen Jia Guanxiong Ha Hanwei Wang Haoyu Ma Hang Chen State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China College of Cyber Science Nankai University Tianjin China Tianjin Key Laboratory of Network and Data Security Technology Tianjin China Zhejiang Lab Hangzhou China The School of Cyber Engineering Xidian University Xi’an China

The encrypted deduplication can provide both storage savings and data confidentiality for cloud storage systems. Convergent encryption (CE) is a well-known solution for encrypted deduplication, but it brings huge computation and storage overheads for key management. MinHash encryption is an effective solution to this issue. It reduces the number of keys by grouping multiple consecutive chunks into segments and generating one key for each segment. However, MinHash encryption does not take full advantage of the segment similarity, which can be used to further reduce the overhead for key management. To this end, we augment MinHash encryption with Bloom filter and Locality Sensitivity Hash (LSH) to design an enhanced MinHash encryption scheme. Firstly, our scheme generates a sketch for each segment based on the Bloom filter, and projects sketches to the points in a hash table through LSH functions. Secondly, we detect the segment similarity by the distance between points, and similar segments are grouped into super-segments. Finally, we combine MinHash encryption and server-aided message-locked encryption to encrypt the super-segments for achieving encrypted deduplication. We conduct trace-driven experiments using a realworld dataset. Compared with MinHash encryption, our scheme has higher storage efficiency and better encryption performance.

关键词： Privacy Maximum likelihood estimation Data privacy Cloud computing Sensitivity Encryption

CMW-Net: Learning a Class-Aware Sample Weighting Mapping for Robust Deep Learning

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arXiv 2022年

作者： Shu, Jun Yuan, Xiang Meng, Deyu Xu, Zongben School of Mathematics and Statistics Ministry of Education Key Lab of Intelligent Networks and Network Security Xi’an Jiaotong University Shaanxi China Peng Cheng Laboratory Guangdong Shenzhen China The Macau Institute of Systems Engineering Macau University of Science and Technology Taipa China

Modern deep neural networks (DNNs) can easily overfit to biased training data containing corrupted labels or class imbalance. Sample re-weighting methods are popularly used to alleviate this data bias issue. Most current methods, however, require manually pre-specifying the weighting schemes as well as their additional hyper-parameters relying on the characteristics of the investigated problem and training data. This makes them fairly hard to be generally applied in practical scenarios, due to their significant complexities and inter-class variations of data bias situations. To address this issue, we propose a meta-model capable of adaptively learning an explicit weighting scheme directly from data. Specifically, by seeing each training class as a separate learning task, our method aims to extract an explicit weighting function with sample loss and task/class feature as input, and sample weight as output, expecting to impose adaptively varying weighting schemes to different sample classes based on their own intrinsic bias characteristics. Synthetic and real data experiments substantiate the capability of our method on achieving proper weighting schemes in various data bias cases, like the class imbalance, feature-independent and dependent label noise scenarios, and more complicated bias scenarios beyond conventional cases. Besides, the task-transferability of the learned weighting scheme is also substantiated, by readily deploying the weighting function learned on relatively smaller-scale CIFAR-10 dataset on much larger-scale full WebVision dataset. A performance gain can be readily achieved compared with previous state-of-the-art ones without additional hyper-parameter tuning and meta gradient descent step. The general availability of our method for multiple robust deep learning issues, including partial-label learning, semi-supervised learning and selective classification, has also been validated. Code for reproducing our experiments is available at https://github

关键词： Deep neural networks

Sketch and customize: A counterfactual story generator

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arXiv 2021年

作者： Hao, Changying Pang, Liang Lan, Yanyan Wang, Yan Guo, Jiafeng Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China Tencent AI Lab United States

Recent text generation models are easy to generate relevant and fluent text for the given text, while lack of causal reasoning ability when we change some parts of the given text. Counterfactual story rewriting is a recently proposed task to test the causal reasoning ability for text generation models, which requires a model to predict the corresponding story ending when the condition is modified to a counterfactual one. Previous works have shown that the traditional sequence-to-sequence model cannot well handle this problem, as it often captures some spurious correlations between the original and counterfactual endings, instead of the causal relations between conditions and endings. To address this issue, we propose a sketch-and-customize generation model guided by the causality implicated in the conditions and endings. In the sketch stage, a skeleton is extracted by removing words which are conflict to the counterfactual condition, from the original ending. In the customize stage, a generation model is used to fill proper words in the skeleton under the guidance of the counterfactual condition. In this way, the obtained counterfactual ending is both relevant to the original ending and consistent with the counterfactual condition. Experimental results show that the proposed model generates much better endings, as compared with the traditional sequence-to-sequence model. © 2021, CC BY-NC-ND.

关键词：

Multi-classification of Android Applications Based on Convolutional Neural networks 20

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Multi-classification of Android Applications Based on Convol...

4th International Conference on Computer science and Application Engineering, CSAE 2020

作者： Wang, Zhiqiang Li, Gefei Chi, Yaping Beijing Electronic Science and Technology Institute Beijing China and State Information Center and Key Lab of Information Network Security Ministry of Public Security Beijing China State Information Center Beijing China Beijing Electronic Science and Technology Institute Beijing China

ISBN: (纸本)9781450377720

Google Play App Store, Huawei App Market, Xiaomi App Store, etc. are the main sources for users to download applications. The application category classification is usually based on the category specified by the developer when uploading or by analyzing the description provided by the developer. However, the process is easily manipulated by malicious developers to evade detection. With the popularity of Android phones, the number of Android applications has exploded. How to quickly and accurately classify applications automatically and improve management efficiency is crucial. This paper proposed a multi-classification framework for Android applications based on convolutional neural networks. By extracting multiple static features, we combined natural language processing and deep learning to achieve a classification accuracy of 99.9064%, and the training time was only 158s. © 2020 ACM.

关键词： Static analysis

青藏高原高寒草地植物的群落性状和功能在降水梯度上调控了生物量的分配权衡

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Journal of Plant Ecology 2023年第5期16卷 44-57页

作者： Le Sun Jian Sun Jun-Xi Wu Zi-Yin Du You-Jun Chen Yi Wang Miao Liu Wen-Cheng Li Er-Yuan Liang Key Laboratory of Tibetan Plateau Earth System Environment and ResourcesInstitute of Tibetan Plateau ResearchChinese Academy of SciencesBeijing 100101China Key Laboratory of Ecosystem Network Observation and Modelling Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 1oo1o1China School of Geographical Sciences China West Normal UniversityNanchong 637002China Institute of the Qinghai-Tibetan Plateau Southwest Minzu UniversityChengdu 610500China School of Life Sciences and School of Ecology State Key Lab of Biological ControlSun Yat-sen UniversityGuangzhou 510275China College of Grassland Science and Technology China Agricultural UniversityBeijing 100193China

了解在不同生态系统中植物生物量分配的调控机制是阐明植物适应性策略的重要理论基础。目前,虽然生境条件对植物生物量分配的影响已被广泛研究,但对植物群落性状和功能(plant community traits and functions,PCTF)如何调控生物量分配,... 详细信息

了解在不同生态系统中植物生物量分配的调控机制是阐明植物适应性策略的重要理论基础。目前,虽然生境条件对植物生物量分配的影响已被广泛研究,但对植物群落性状和功能(plant community traits and functions,PCTF)如何调控生物量分配,特别是在高寒草地生态系统中的研究却较少。本研究计算了在群落水平上的5种叶片功能性状的群落加权平均值(community-weighted means,CWM),并利用相关分析、方差分解分析和结构方程模型探讨了PCTF与生物量权衡之间的关系。研究结果发现,高寒草甸(alpine meadow,AM)和高寒草原(alpine steppe,AS)的权衡值都大于零,但两者存在显著差异,AM的权衡值(0.203)明显高于AS(0.088)。此外,决定AS生物量分配的关键因素是物种丰富度(0.69)和叶片干物质含量(CWM_(LDMC),0.42),而在AM中,关键因素是叶片干物质含量(CWM_(LDMC),0.48)和叶片碳含量(CWM_(LC)-0.45)。值得注意的是,无论AS中的CWM_(LDMC)和物种丰富度,还是AM中的CWM_(LDMC)和CWM_(LC)都主要受气候因素中的降水调节。总而言之,降水往往通过对PCTF的影响来调控高寒草地的生物量分配,因此强调了PCTF在调节降水梯度上植物生物量分配策略的重要性。

关键词：群落加权平均权衡生物量分配植物群落性状和功能高寒草地青藏高原

MutexMatch: Semi-Supervised Learning with Mutex-Based Consistency Regularization

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arXiv 2022年

作者： Duan, Yue Zhao, Zhen Qi, Lei Wang, Lei Zhou, Luping Shi, Yinghuan Gao, Yang The National Key Laboratory for Novel Software Technology The National Institute of Healthcare Data Science Nanjing University Nanjing210023 China The School of Electrical and Information Engineering The University of Sydney SydneyNSW2006 Australia The School of Computer Science and Engineering The Key Lab of Computer Network and Information Integration Ministry of Education Southeast University Nanjing211189 China The School of Computing and Information Technology University of Wollongong WollongongNSW2522 Australia

The core issue in semi-supervised learning (SSL) lies in how to effectively leverage unlabeled data, whereas most existing methods tend to put a great emphasis on the utilization of high-confidence samples yet seldom fully explore the usage of low-confidence samples. In this paper, we aim to utilize low-confidence samples in a novel way with our proposed mutex-based consistency regularization, namely MutexMatch. Specifically, the high-confidence samples are required to exactly predict "what it is" by conventional True-Positive Classifier, while low-confidence samples are employed to achieve a simpler goal — to predict with ease "what it is not" by True-Negative Classifier. In this sense, we not only mitigate the pseudo-labeling errors but also make full use of the low-confidence unlabeled data by consistency of dissimilarity degree. MutexMatch achieves superior performance on multiple benchmark datasets, i.e., CIFAR-10, CIFAR-100, SVHN, STL-10, mini-ImageNet and Tiny-ImageNet. More importantly, our method further shows superiority when the amount of labeled data is scarce, e.g., 92.23% accuracy with only 20 labeled data on CIFAR-10. Our code and model weights have been released at https://***/NJUyued/MutexMatch4SSL. Copyright © 2022, The Authors. All rights reserved.

关键词： Benchmarking

Guaranteed Tensor Recovery Fused Low-rankness and Smoothness

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arXiv 2023年

作者： Wang, Hailin Peng, Jiangjun Qin, Wenjin Wang, Jianjun Meng, Deyu School of Mathematics and Statistics Xi’an Jiaotong University Shanxi Xi'An710049 China School of Mathematics and Statistics Southwest University Chongqing400715 China School of Mathematics and Statistics Ministry of Education Key Lab of Intelligent Networks and Network Security Xi’an Jiaotong University Shanxi Xi'An China Macau Institute of Systems Engineering Macau University of Science and Technology China

Vast visual data like multi-spectral images and multi-frame videos are essentially with the tensor format. However, due to the defects of signal acquisition equipments, the practically collected tensor data are always with evident degradations like corruptions or missing values. The tensor data recovery task has thus attracted much research attention in recent years. Solving such an ill-posed problem generally requires to explore intrinsic prior structures underlying tensor data, and formulate them as certain forms of regularization terms for guiding a sound estimate of the restored tensor. Recent research have made significant progress by adopting two insightful tensor priors, i.e., global low-rankness (L) and local smoothness (S) across different tensor modes, which are always encoded as a sum of two separate regularization terms into the recovery models. However, unlike the primary theoretical developments on low-rank tensor recovery, these joint "L+S" models have no theoretical exact-recovery guarantees yet, making the methods lack reliability in real practice. To this crucial issue, in this work, we build a unique regularization term, which essentially encodes both L and S priors of a tensor simultaneously. Especially, by equipping this single regularizer into the recovery models, we can rigorously prove the exact recovery guarantees for two typical tensor recovery tasks, i.e., tensor completion (TC) and tensor robust principal component analysis (TRPCA). To the best of our knowledge, this should be the first exact-recovery results among all related "L+S" methods for tensor recovery. We further propose ADMM algorithms for solving the proposed models, and prove their fine convergence properties. Significant recovery accuracy improvements over many other SOTA methods in several TC and TRPCA tasks with various kinds of visual tensor data are observed in extensive experiments. Typically, our method achieves a workable performance when the missing rate is extremely

关键词： Tensors

Proposal for stable atom trapping on a GaN-on-Sapphire chip

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arXiv 2022年

作者： Liu, Aiping Xu, Lei Xu, Xin-Biao Chen, Guang-Jie Zhang, Pengfei Xiang, Guo-Yong Guo, Guang-Can Wang, Qin Zou, Chang-Ling Institute of quantum information and technology Nanjing University of Posts and Telecommunications Nanjing210003 China CAS Key Laboratory of Quantum Information University of Science and Technology of China Hefei230026 China State Key Laboratory of Quantum Optics and Quantum Optics Devices Institute of Opto-Electronics Shanxi University Taiyuan030006 China Broadband wireless communication and sensor network technology Key lab of Ministry of Education Nanjing University of Posts and Telecommunications Nanjing210003 China CAS Center for Excellence in Quantum Information and Quantum Physics University of Science and Technology of China Hefei230026 China

The hybrid photon-atom integrated circuits, which include photonic microcavities and trapped single neutral atom in their evanescent field, are of great potential for quantum information processing. In this platform, the atoms provide the single-photon nonlinearity and long-lived memory, which are complementary to the excellent passive photonics devices in conventional quantum photonic circuits. In this work, we propose a stable platform for realizing the hybrid photon-atom circuits based on an unsuspended photonic chip. By introducing high-order modes in the microring, a feasible evanescent-field trap potential well ∼ 0.3mK could be obtained by only 10mW-level power in the cavity, compared with 100mW-level power required in the scheme based on fundamental modes. Based on our scheme, stable single atom trapping with relatively low laser power is feasible for future studies on high-fidelity quantum gates, single-photon sources, as well as many-body quantum physics based on a controllable atom array in a microcavity. © 2022, CC BY.

关键词： Atoms

Multi-grating design for integrated single-atom trapping, manipulation and readout

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arXiv 2022年

作者： Liu, Aiping Liu, Jiawei Peng, Wei Xu, Xin-Biao Chen, Guang-Jie Ren, Xifeng Wang, Qin Zou, Chang-Ling Institute of Quantum Information and Technology Nanjing University of Posts and Telecommunications Nanjing210003 China CAS Key Laboratory of Quantum Information University of Science and Technology of China Hefei230026 China Broadband Wireless Communication and Sensor Network Technology Key Lab of Ministry of Education Nanjing University of Posts and Telecommunications Nanjing210003 China CAS Center for Excellence in Quantum Information and Quantum Physics University of Science and Technology of China Hefei230026 China State Key Laboratory of Quantum Optics and Quantum Optics Devices Institute of Opto-Electronics Shanxi University Taiyuan030006 China

An on-chip multi-grating device is proposed to interface single-atoms and integrated photonic circuits, by guiding and focusing lasers to the area with ∼10 μm above the chip for trapping, state manipulation and readout of single Rubidium atoms. For the optical dipole trap, two 850 nm laser beams are diffracted and overlapped to form a lattice of single-atom dipole trap, with the diameter of optical dipole trap around 2.7 μm. Similar gratings are designed for guiding 780 nm probe laser to excite and also collect the fluorescence of 87Rb atoms. Such a device provides a compact solution for future applications of single atoms, including the single photon source, single-atom quantum register, and sensor. © 2022, CC BY.

关键词： Atoms