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Static tainting extraction approach based on information flow graph for personally identifiable information

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Science China(Information Sciences) 2020年第3期63卷 156-172页

作者： Yi LIU lejian liao Tian SONG School of Computer Science and Technology Beijing Institute of Technology College of Mathematics and Computer Science Yan'an University

Personally identifiable information(PII) is widely used for many aspects such as network privacy leak detection, network forensics, and user portraits. Internet service providers(ISPs) and administrators are usually concerned with whether PII has been extracted during the network transmission process. However,most studies have focused on the extractions occurring on the client side and server side. This study proposes a static tainting extraction approach that automatically extracts PII from large-scale network traffic without requiring any manual work and feedback on the ISP-level network traffic. The proposed approach does not deploy any additional applications on the client side. The information flow graph is drawn via a tainting process that involves two steps: inter-domain routing and intra-domain infection that contains a constraint function(CF) to limit the "over-tainting". Compared with the existing semantic-based approach that uses network traffic from the ISP, the proposed approach performs better, with 92.37% precision and 94.04%recall. Furthermore, three methods that reduce the computing time and the memory overhead are presented herein. The number of rounds is reduced to 0.0883%, and the execution time overhead is reduced to 0.0153%of the original approach.

关键词： personally identifiable information network privacy leak detection static tainting network traffic analysis information flow graph inter-domain routing intra-domain infection constraint function

PSVM: a preference-enhanced SVM model using preference data for classification

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Science China(Information Sciences) 2017年第12期60卷 165-178页

作者： Lerong MA Dandan SONG lejian liao Jingang WANG Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications School of Computer Science and Technology Beijing Institute of Technology College of Mathematics and Computer Science Yan'an University Search Business Department Alibaba Group

Classification is an essential task in data mining, machine learning and pattern recognition *** classification models focus on distinctive samples from different categories. There are fine-grained differences between data instances within a particular category. These differences form the preference information that is essential for human learning, and, in our view, could also be helpful for classification models. In this paper, we propose a preference-enhanced support vector machine(PSVM), that incorporates preference-pair data as a specific type of supplementary information into SVM. Additionally, we propose a two-layer heuristic sampling method to obtain effective preference-pairs, and an extended sequential minimal optimization(SMO)algorithm to fit PSVM. To evaluate our model, we use the task of knowledge base acceleration-cumulative citation recommendation(KBA-CCR) on the TREC-KBA-2012 dataset and seven other datasets from UCI,Stat Lib and ***. The experimental results show that our proposed PSVM exhibits high performance with official evaluation metrics.

关键词： preference SVM classification sampling sequential minimal optimization(SMO)

A Bayesian Recommender Model for User Rating and Review Profiling

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Tsinghua Science and Technology 2015年第6期20卷 634-643页

作者： Mingming Jiang Dandan Song lejian liao Feida Zhu School of Computer Science and Technology Beijing Institute of Technology School of Information Systems Singapore Management University

Intuitively, not only do ratings include abundant information for learning user preferences, but also reviews accompanied by ratings. However, most existing recommender systems take rating scores for granted and discard the wealth of information in accompanying reviews. In this paper, in order to exploit user profiles’ information embedded in both ratings and reviews exhaustively, we propose a Bayesian model that links a traditional Collaborative Filtering(CF) technique with a topic model seamlessly. By employing a topic model with the review text and aligning user review topics with 'user attitudes'(i.e., abstract rating patterns) over the same distribution, our method achieves greater accuracy than the traditional approach on the rating prediction task. Moreover, with review text information involved, latent user rating attitudes are interpretable and 'cold-start' problem can be *** property qualifies our method for serving as a 'recommender' task with very sparse datasets. Furthermore,unlike most related works, we treat each review as a document, not all reviews of each user or item together as one document, to fully exploit the reviews’ information. Experimental results on 25 real-world datasets demonstrate the superiority of our model over state-of-the-art methods.

关键词： collaborative filtering topic model recommender sy

A Latent Entity-Document Class Mixture of Experts Model for Cumulative Citation Recommendation

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Tsinghua Science and Technology 2018年第6期23卷 660-670页

作者： Lerong Ma lejian liao DANDan Song Jingang Wang Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing Institute of Technology College of Mathematics and Computer Science Yan'an University

Knowledge Bases （KBs） are valuable resources of human knowledge which contribute to many applications. However, since they are manually maintained, there is a big lag between their contents and the upto-date information of entities. Considering a target entity in KBs, this paper investigates how Cumulative Citation Recommendation （CCR） can be used to effectively detect its worthy-citation documents in large volumes of stream data. Most global relevant models only consider semantic and temporat features of entity-document instances, which does not sufficiently exploit prior knowledge underlying entity-document instances. To tackle this problem, we present a Mixture of Experts （ME） model by introducing a latent layer to capture relationships between the entity-document instances and their latent class information. An extensive set of experiments was conducted on TREC-KBA-2013 dataset. The results show that the model can significantly achieve a better performance gain compared to state-of-the-art models in CCR.

关键词： knowledge base acceleration cumulative citation recommendation Mixture of Experts (ME) LatentEntity-Document Classes (LEDCs)

QACtools: A Quality Assessment and Quality Control Tool for Next-Generation Sequencing Data

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QACtools: A Quality Assessment and Quality Control Tool for ...

Intention Enhanced Dual Heterogeneous Graph Attention Network for Sequential Recommendation

2015年中国智能自动化学术会议

作者： Dandan Song Ning Li lejian liao Beijing Lab of Intelligent Information Technology School of Computer Science and Technology Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing Institute of Technology

In recent years, next-generation sequencing technologies have been widely used, and are rapidly changing the landscape of genetics with unimaginable speed. However, the preliminary quality control regulations including trimming adaptor sequences, quality statistics, and depth and coverage statistics need to be applied to raw reads before further analysis. QACtools implements various utilities for processing raw reads in the FASTQ format and alignments in the BAM *** analyzes some relevant properties of next-generation sequencing data such as average read length, base quality scores, and so on. Additionally, to our knowledge,none of the other tools support depth and coverage statistic that have been provided in our tool for analysis of large DNA sequencing data. It is crucial for overcoming errors in base calling and assembly. We suppose tool is useful for the quality control of NGS data to perform further analysis. This software is an open source application freely available at https://***/projects/qactools/.

关键词： NGS data analysis Quality control FASTQ format BAM format

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Intention Enhanced Dual Heterogeneous Graph Attention Networ...

Universally Composable Symbolic Analysis of Group Key Exchange Protocol

2021中国智能自动化大会(CIAC2021)

作者： Yongyu Zhou Dandan Song lejian liao Heyan Huang School of Computer Science Beijing Institute of Technology

Sequential recommendation plays a vital role in many web applications, aiming to predict users’ next actions based on their historical sequential behaviors. Efficiently learning the features of items and understanding user’s intentions are pivotal for sequential recommendation. However, due to the diversity of items and the sparseness of user’s interaction with items, it’s challenging to accurately learn the features of items through sparse interaction data. In addition, users usually have shifting intentions when interacting with items, which makes it difficult to understand users’ intentions. To this end, we propose a novel intention enhanced dual heterogeneous graph attention network(IE-DHGAT)for sequential recommendation. Specifically, we construct an extensible heterogeneous graph, which contains items and items’ various attributes,and we design a dual graph attention network to learn the features of items via explicitly incorporating item’s various attribute information into item embeddings. Further, we propose an intention enhanced attention layer to efficiently capture users’ shifting intentions through computing the correlation between items and discriminating different intention areas in users’ interaction sequences. We conduct extensive experiments on three real-world datasets and the results demonstrate that our proposed approach achieves better performance than the state-of-the-art methods.

关键词： Sequential recommendation Heterogeneous graph User intention

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China Communications 2011年第2期8卷 59-65页

作者：张子剑祝烈煌廖乐健 School of Computer Science and Technology Beijing Institute of Technology

Canetti and Herzog have already proposed universally composable symbolic analysis(UCSA) to analyze mutual authentication and key exchange protocols. However,they do not analyze group key exchange protocol. Therefore,this paper explores an approach to analyze group key exchange protocols,which realize automation and guarantee the soundness of cryptography. Considered that there exist many kinds of group key exchange protocols and the participants’ number of each protocol is arbitrary. So this paper takes the case of Burmester-Desmedt(BD) protocol with three participants against passive adversary(3-BD-Passive) . In a nutshell,our works lay the root for analyzing group key exchange protocols automatically without sacrificing soundness of cryptography.

关键词： universally composable symbolic analysis universally composable mapping algorithm burmester-desmedt protocol group key exchange protocol

一种可抵抗内部攻击的高效群组密钥协商协议编译器

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东南大学学报（自然科学版） 2010年第A2期40卷 313-317页

作者：张子剑祝烈煌廖乐健北京理工大学计算机学院北京100081

目前设计可抵抗内部攻击的群组密钥协商协议需要调用2个不同的协议编译器,使得协议的执行效率较低而且协议设计的复杂度较高,为此,通过改进上述2个协议编译器,提出了一种新的编译器.该编译器只需对群组密钥协商协议进行一次编译,即可生... 详细信息

目前设计可抵抗内部攻击的群组密钥协商协议需要调用2个不同的协议编译器,使得协议的执行效率较低而且协议设计的复杂度较高,为此,通过改进上述2个协议编译器,提出了一种新的编译器.该编译器只需对群组密钥协商协议进行一次编译,即可生成具有抵抗内部攻击能力的群组密钥协商协议.然后在随机预言机模型下,针对提出的编译器所生成的协议进行了安全性分析,并严格证明了生成的协议是具有抵抗内部攻击能力的群组密钥协商协议.最后对比分析表明,利用提出的编译器生成的协议与现有编译器生成的协议相比,具有较高的效率和较低的设计复杂度.

关键词：群组密钥协商编译器内部攻击可证安全

基于检索相关度拓展的网页排名技术:IR-REL

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情报杂志 2006年第4期25卷 92-94页

作者：许颖廖乐健北京理工大学计算机科学与工程系北京100081

SEO SPAM和飞速发展的网页规模要求更优的网页排名技术,在研究链接分析技术PageRank及互信息模型后,提出了检索相关度算法(IR-REL),构建相关词链表计算网页的布尔权重。该排名同时将权威度和相关度作为影响因子,可提高检索相关度,降低... 详细信息

SEO SPAM和飞速发展的网页规模要求更优的网页排名技术,在研究链接分析技术PageRank及互信息模型后,提出了检索相关度算法(IR-REL),构建相关词链表计算网页的布尔权重。该排名同时将权威度和相关度作为影响因子,可提高检索相关度,降低关键词作弊影响。文章给出了具体的排名策略、实验数据和系统模型。

关键词：网页排名优化作弊 IR-REL算法网页排名 PageRank