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Optimizing Location Quality in Privacy Preserving Crowdsensing

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Optimizing Location Quality in Privacy Preserving Crowdsensi...

2019 IEEE Global Communications Conference (GLOBECOM)

作者： Yuhui Zhang Ming Li Dejun Yang Jian Tang Guoliang Xue Colorado School of Mines Golden CO Colorado School of Mines Golden CO USA Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China Syracuse University Syracuse NY Arizona State University Tempe AZ

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

Crowdsensing enables a wide range of data collection, where the data are usually tagged with private locations. Protecting users' location privacy has been a central issue. The study of various location perturbation techniques for protecting users' location privacy has received widespread attention. Despite the huge promise and considerable attention, the location perturbation operation causes inevitable location errors, which can diminish the location quality of the crowdsensing results. Provable good algorithms that consider location quality in privacy preserving crowdsensing from optimization perspectives are still lacking in the literature. In this paper, we investigate the problem of location quality optimization in privacy preserving crowdsensing, which is to minimize the location quality desegregation, while protecting all users' location privacy. We present an optimal algorithm OLQDM for this problem. Extensive simulations demonstrate that OLQDM significantly outperforms an existing algorithm in terms of the location quality and SSE.

关键词： Privacy Degradation Sensors Perturbation methods Optimization Minimization Mobile handsets

Consistent and Complementary Graph Regularized Multi-view Subspace Clustering

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arXiv 2020年

作者： Zheng, Qinghai Zhu, Jihua Li, Zhongyu Pang, Shanmin Wang, Jun Chen, Lei School of Software Engineering Xi’an Jiaotong University Xi’an710049 China Shanghai Institute for Advanced Communication and Data Science School of Communication and Information Engineering Shanghai University Shanghai200444 China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing210023 China

This study investigates the problem of multi-view clustering, where multiple views contain consistent information and each view also includes complementary information. Exploration of all information is crucial for good multi-view clustering. However, most traditional methods blindly or crudely combine multiple views for clustering and are unable to fully exploit the valuable information. Therefore, we propose a method that involves consistent and complementary graph-regularized multi-view subspace clustering (GRMSC), which simultaneously integrates a consistent graph regularizer with a complementary graph regularizer into the objective function. In particular, the consistent graph regularizer learns the intrinsic affinity relationship of data points shared by all views. The complementary graph regularizer investigates the specific information of multiple views. It is noteworthy that the consistent and complementary regularizers are formulated by two different graphs constructed from the first-order proximity and second-order proximity of multiple views, respectively. The objective function is optimized by the augmented Lagrangian multiplier method in order to achieve multi-view clustering. Extensive experiments on six benchmark datasets serve to validate the effectiveness of the proposed method over other state-of-the-art multi-view clustering methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Clustering algorithms

WISERNet: Wider Separate-then-reunion Network for Steganalysis of Color Images

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arXiv 2018年

作者： Zeng, Jishen Tan, Shunquan Liu, Guangqing Li, Bin Huang, Jiwu Guangdong Key Laboratory of Intelligent Information Processing National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China College of Computer Science and Software Engineering Shenzhen University Guangdong Key Lab. of Intelligent Info. Processing and Shenzhen Key Laboratory of Media Security Shenzhen University Shenzhen518060 China Peng Cheng Laboratory Shenzhen518052 China

Until recently, deep steganalyzers in spatial domain have been all designed for gray-scale images. In this paper, we propose WISERNet (the wider separate-then-reunion network) for steganalysis of color images. We provide theoretical rationale to claim that the summation in normal convolution is one sort of "linear collusion attack" which reserves strong correlated patterns while impairs uncorrelated noises. Therefore in the bottom convolutional layer which aims at suppressing correlated image contents, we adopt separate channel-wise convolution without summation instead. Conversely, in the upper convolutional layers we believe that the summation in normal convolution is beneficial. Therefore we adopt united normal convolution in those layers and make them remarkably wider to reinforce the effect of "linear collusion attack". As a result, our proposed wide-and-shallow, separate-then-reunion network structure is specifically suitable for color image steganalysis. We have conducted extensive experiments on color image datasets generated from BOSSBase raw images and another large-scale dataset which contains 100,000 raw images, with di_erent demosaicking algorithms and downsampling algorithms. The experimental results show that our proposed network outperforms other state-of-the-art color image steganalytic models either hand-crafted or learned using deep networks in the literature by a clear margin. Specifically, it is noted that the detection performance gain is achieved with less than half the complexity compared to the most advanced deeplearning steganalyzer as far as we know, which is scarce in the literature. Copyright © 2018, The Authors. All rights reserved.

关键词： Steganography

Network-coding-based video transmission via WiFi direct in local mobile networks

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Network-coding-based video transmission via WiFi direct in l...

International Conference on intelligent System and Knowledge Engineering, ISKE

作者： Lei Wang Qing Wang Zhongwei Sun Jun Yin Lijie Xu Yuwang Yang Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing P.R. China School of Computer Nanjing University of Posts and Telecommunications Nanjing P.R. China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing P.R. China

It will incur high expense by sharing large-scale files (such as video) with others through commercial 4G networks. When the users are located at the same area, the traffic cost could be saved via the technology of WiFi direct. In this paper, a network-coding-based video transmission scheme is proposed for such scenario. In the scheme, when a source user needs to transmit a video to others, it equally splits the original file into multiple segments. When a neighbour connects to it and requests a segment, the user will linearly generate a new segment with all the segments it buffers, and then send the re-encoded segment to other neighbours. After the neighbours receive all or part of the segments, they could become a new source and send data to their own neighbours through the technology of WiFi direct. The results show that compared with the traditional scheme, the transmission scheme based on network coding could be used to complete transmission at a higher rate.

关键词： Switches Analytical models Network coding Streaming media Wireless fidelity Mobile handsets Mobile computing

VFL: A Verifiable Federated Learning with Privacy-Preserving for big data in Industrial IoT

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arXiv 2020年

作者： Fu, Anmin Zhang, Xianglong Xiong, Naixue Gao, Yansong Wang, Huaqun School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Northeastern State University Department of Mathematics and Computer Science TahlequahOK United States Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing210023 China

Due to the strong analytical ability of big data, deep learning has been widely applied to model the collected data in industrial IoT. However, for privacy issues, traditional data-gathering centralized learning is not applicable to industrial scenarios sensitive to training sets. Recently, federated learning has received widespread attention, since it trains a model by only relying on gradient aggregation without accessing training sets. But existing researches reveal that the shared gradient still retains the sensitive information of the training set. Even worse, a malicious aggregation server may return forged aggregated gradients. In this paper, we propose the VFL, verifiable federated learning with privacy-preserving for big data in industrial IoT. Specifically, we use Lagrange interpolation to elaborately set interpolation points for verifying the correctness of the aggregated gradients. Compared with existing schemes, the verification overhead of VFL remains constant regardless of the number of participants. Moreover, we employ the blinding technology to protect the privacy of the gradients submitted by the participants. If no more than n-2 of n participants collude with the aggregation server, VFL could guarantee the encrypted gradients of other participants not being inverted. Experimental evaluations corroborate the practical performance of the presented VFL framework with high accuracy and efficiency. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

Contrastive Learning for Robust Android Malware Familial Classification

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IEEE Transactions on Dependable and Secure Computing 2022年 1-14页

作者： Wu, Yueming Dou, Shihan Zou, Deqing Yang, Wei Qiang, Weizhong Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai China University of Texas at Dallas Dallas USA National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Due to its open-source nature, Android operating system has been the main target of attackers to exploit. Malware creators always perform different code obfuscations on their apps to hide malicious activities. Features extracted from these obfuscated samples through program analysis contain many useless and disguised features, which leads to many false negatives. To address the issue, in this paper, we demonstrate that obfuscation-resilient malware family analysis can be achieved through contrastive learning. The key insight behind our analysis is that contrastive learning can be used to reduce the difference introduced by obfuscation while amplifying the difference between malware and other types of malware. Based on the proposed analysis, we design a system that can achieve robust and interpretable classification of Android malware. To achieve robust classification, we perform contrastive learning on malware samples to learn an encoder that can automatically extract robust features from malware samples. To achieve interpretable classification, we transform the function call graph of a sample into an image by centrality analysis. Then the corresponding heatmaps can be obtained by visualization techniques. These heatmaps can help users understand why the malware is classified as this family. We implement IFDroid and perform extensive evaluations on two datasets. Experimental results show that IFDroid is superior to state-of-the-art Android malware familial classification systems. Moreover, IFDroid is capable of maintaining a 98.4% F1 on classifying 69,421 obfuscated malware samples. IEEE

关键词： Semantics

A Multimodal Ecological Civilization Pattern Recommendation Method Based on Large Language Models and Knowledge Graph

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arXiv 2023年

作者： Yu, Zhihang Wang, Shu Zhu, Yunqiang Zou, Zhiqiang College of Computer Nanjing University of Posts and Telecommunications Nanjing210023 China State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing210023 China

The Ecological Civilization Pattern Recommendation System (ECPRS) aims to recommend suitable ecological civilization patterns for target regions, promoting sustainable development and reducing regional disparities. However, the current representative recommendation methods are not suitable for recommending ecological civilization patterns in a geographical context. There are two reasons for this. Firstly, regions have spatial heterogeneity, and the (ECPRS)needs to consider factors like climate, topography, vegetation, etc., to recommend civilization patterns adapted to specific ecological environments, ensuring the feasibility and practicality of the recommendations. Secondly, the abstract features of the ecological civilization patterns in the real world have not been fully utilized., resulting in poor richness in their embedding representations and consequently, lower performance of the recommendation system. Considering these limitations, we propose the ECPR-MML method. Initially, based on the novel method UGPIG, we construct a knowledge graph to extract regional representations incorporating spatial heterogeneity features. Following that, inspired by the significant progress made by Large Language Models (LLMs) in the field of Natural Language processing (NLP), we employ Large LLMs to generate multimodal features for ecological civilization patterns in the form of text and images. We extract and integrate these multimodal features to obtain semantically rich representations of ecological civilization. Through extensive experiments, we validate the performance of our ECPR-MML model. Our results show that F1@5 is 2.11% higher compared to state-of-the-art models, 2.02% higher than NGCF, and 1.16% higher than UGPIG. Furthermore, multimodal data can indeed enhance recommendation performance. However, the data generated by LLM is not as effective as real data to a certain extent." Copyright © 2023, The Authors. All rights reserved.

关键词： Recommender systems

PhotoRedshift-MML: a multimodal machine learning method for estimating photometric redshifts of quasars

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arXiv 2022年

作者： Hong, Shuxin Zou, Zhiqiang Luo, A-Li Kong, Xiao Yang, Wenyu Chen, Yanli College of Computer Nanjing University of Posts and Telecommunications Nanjing210023 China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing210023 China CAS Key Laboratory of Optical Astronomy National Astronomical Observatories Beijing100101 China School of Astronomy and Space Science University of Chinese Academy of Sciences Beijing100049 China

We propose a Multimodal Machine Learning method for estimating the Photometric Redshifts of quasars (PhotoRedshift-MML for short), which has long been the subject of many investigations. Our method includes two main models, i.e. the feature transformation model by multimodal representation learning, and the photometric redshift estimation model by multimodal transfer learning. The prediction accuracy of the photometric redshift was significantly improved owing to the large amount of information offered by the generated spectral features learned from photometric data via the MML. A total of 415,930 quasars from Sloan Digital Sky Survey (SDSS) data Release 17, with redshifts between 1 and 5, were screened for our experiments. We used |Δz| = |(zphot− zspec)/(1 + zspec)| to evaluate the redshift prediction and demonstrated a 4.04% increase in accuracy. With the help of the generated spectral features, the proportion of data with |Δz| Copyright © 2022, The Authors. All rights reserved.

关键词： Surveys

Adaptive Teacher-and-Student Model for Heterogeneous Domain Adaptation

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Adaptive Teacher-and-Student Model for Heterogeneous Domain ...

IEEE International Conference on data Mining (ICDM)

作者： Yunyun Wang Xuzhang Chen Songcan Chen Hui Xue College of Computer Science Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts & Telecommunications Nanjing China College of Computer Science & Technology/AI MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing University of Aeronautics & Astronautics Nanjing China College of Computer Science and Engineering Southeast University Nanjing China

In heterogeneous domain adaptation (HDA), since the feature spaces of the source and target domains are different, knowledge transfer from the source to the target domain is really challenging. How to align the different feature spaces and then adaptively transfer the related knowledge is critical for HDA. In this paper, we develop an adaptive teacher-and-student model for heterogeneous domain adaptation (AtsHDA). In AtsHDA, the source domain as a teacher and the target domain as a student are aligned or co-adapted to each other first, so that their correlation can be maximized. Then the target domain adaptively learns from the source domain. Specifically, there is a balance between the learning by the target domain itself and the instruction from the source domain. That is, when the guidance from the source domain is helpful for learning, the learning of target classifier emphasizes the instruction of source knowledge, and considers its own knowledge more, otherwise. Further, an ensemble method is designed to decide such a balance. Finally, empirical results show that AtsHDA can achieve competitive results compared with the state-of-arts.

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