|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | A Pulse Immunization Model for Inhibiting Malware Propagation in Mobile Wireless Sensor Networks显示文摘Mobile wireless sensor networks(MWSNs)may be under attack due to their large-scale characteristics. One of the main threats is to inject malware into some nodes. To prevent malware from spreading in a largescale MWSN, an effective measure is to immunize susceptible nodes by disseminating and installing security patches. This work suggests a novel modeling framework and some mathematical models based on the pulse differential equation and the epidemic theory, in which the immunization operations are implemented on susceptible nodes in a pulse way. The maximum immunization period of time is derived to minimine the number of immunization operations while ensuring malware extinct over time in the MWSN. The theoretical results are confirmed by extensive simulations. | WANG Xiaoming HE Zaobo ZHANG Lichen | 2014 | Chinese Journal of Electronics2014，23，4： | 8 |
| 2 | Reaction-diffusion modeling of malware propagation in mobile wireless sensor networks显示文摘Mobile Wireless Sensor Networks (MWSNs) are employed in many fields, such as intelligent transportation, community health monitoring, and animal behavior monitoring. However, MWSNs may be vulnerable to malicious interference because of the large-scale characteristics. One of the threats is to inject malware into some nodes, especially mobile nodes. When a contaminated node communicates with its neighbors, multiple copies of the malware are transmitted to its neighbors, which may destroy nodes, block regular communications, or even damage the integrity of regular data packets. This work develops a modeling framework which mathematically characterizes the process of malware propagation in MWSNs based on the theory of reaction-diffusion equation. Our proposed model can efficiently predict the temporal dynamic behavior and spatial distribution of malware propagation over time, so that targeted immunization measures can be taken on infected nodes, whereas most of the existing models for malware propagation can only predict the temporal dynamic behavior rather than the spatial distribution of malware propagation over time. We conduct extensive simulations on large-scale MWSNs to evaluate the proposed model. The simulation results indicate that the proposed model and method are efficient, and that the mobile speed, communication range, and packet transmission rate of nodes are the main factors affecting malware propagation in MWSNs. | WANG XiaoMing HE ZaoBo ZHAO XueQing LIN Chuang PAN Yi CAI ZhiPeng | 2013 | Science China(Information Sciences)2013，56，9： | 4 |
| 3 | A Survey of Federated Learning on Non-IID Data显示文摘Federated learning(FL) is a machine learning paradigm for data silos and privacy protection,which aims to organize multiple clients for training global machine learning models without exposing data to all parties.However,when dealing with non-independently identically distributed(non-ⅡD) client data,FL cannot obtain more satisfactory results than centrally trained machine learning and even fails to match the accuracy of the local model obtained by client training alone.To analyze and address the above issues,we survey the state-of-theart methods in the literature related to FL on non-ⅡD data.On this basis,a motivation-based taxonomy,which classifies these methods into two categories,including heterogeneity reducing strategies and adaptability enhancing strategies,is proposed.Moreover,the core ideas and main challenges of these methods are analyzed.Finally,we envision several promising research directions that have not been thoroughly studied,in hope of promoting research in related fields to a certain extent. | HAN Xuming GAO Minghan WANG Limin HE Zaobo WANG Yanze | 2022 | ZTE Communications2022，20，3： | 0 |
| 4 | Achieving Differential Privacy of Genomic Data Releasing via Belief Propagation显示文摘Privacy preserving data releasing is an important problem for reconciling data openness with individual privacy. The state-of-the-art approach for privacy preserving data release is differential privacy, which offers powerful privacy guarantee without confining assumptions about the background knowledge about attackers. For genomic data with huge-dimensional attributes, however, current approaches based on differential privacy are not effective to handle. Specifically, amount of noise is required to be injected to genomic data with tens of million of SNPs(Single Nucleotide Polymorphisms), which would significantly degrade the utility of released data. To address this problem, this paper proposes a differential privacy guaranteed genomic data releasing method. Through executing belief propagation on factor graph, our method can factorize the distribution of sensitive genomic data into a set of local distributions. After injecting differential-privacy noise to these local distributions, synthetic sensitive data can be obtained by sampling on noise distribution. Synthetic sensitive data and factor graph can be further used to construct approximate distribution of non-sensitive data. Finally, non-sensitive genomic data is sampled from the approximate distribution to construct a synthetic genomic dataset. | Zaobo He Yingshu Li Ji Li Kaiyang Li Qing Cai Yi Liang | 2018 | Tsinghua Science and Technology2018，23，4： | 0 |
| 5 | Optimization-Based Fragmental Transmission Method for Video Data in Opportunistic Networks显示文摘Multimedia data have become popularly transmitted content in opportunistic networks. A large amount of video data easily leads to a low delivery ratio. Breaking up these big data into small pieces or fragments is a reasonable option. The size of the fragments is critical to transmission efficiency and should be adaptable to the communication capability of a network. We propose a novel communication capacity calculation model of opportunistic network based on the classical random direction mobile model, define the restrain facts model of overhead, and present an optimal fragment size algorithm. We also design and evaluate the methods and algorithms with video data fragments disseminated in a simulated environment. Experiment results verified the effectiveness of the network capability and the optimal fragment methods. | Peng Li Xiaoming Wang Junling Lu Lichen Zhang Zaobo He | 2017 | Tsinghua Science and Technology2017，22，4： | 0 |
| 6 | A Local Differential Privacy Trajectory Protection Method Based on Temporal and Spatial Restrictions for Staying Detection显示文摘The widespread availability of GPS has opened up a whole new market that provides a plethora of location-based services.Location-based social networks have become very popular as they provide end users like us with several such services utilizing GPS through our devices.However,when users utilize these services,they inevitably expose personal information such as their ID and sensitive location to the servers.Due to untrustworthy servers and malicious attackers with colossal background knowledge,users'personal information is at risk on these servers.Unfortunately,many privacy-preserving solutions for protecting trajectories have significantly decreased utility after deployment.We have come up with a new trajectory privacy protection solution that contraposes the area of interest for users.Firstly,Staying Points Detection Method based on Temporal-Spatial Restrictions(SPDM-TSR)is an interest area mining method based on temporal-spatial restrictions,which can clearly distinguish between staying and moving points.Additionally,our privacy protection mechanism focuses on the user's areas of interest rather than the entire trajectory.Furthermore,our proposed mechanism does not rely on third-party service providers and the attackers'background knowledge settings.We test our models on real datasets,and the results indicate that our proposed algorithm can provide a high standard privacy guarantee as well as data availability. | Weiqi Zhang Zhenzhen Xie Akshita Maradapu Vera Venkata Sai Qasim Zia Zaobo He Guisheng Yin | 2024 | Tsinghua Science and Technology2024，29，2： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员