scholarly journals A Probabilistic Topic Model based on Local Word Relationships in Overlapped Windows

2019 ◽  
Vol 15 (4) ◽  
pp. 57-70
Author(s):  
Marziea Rahimi ◽  
Morteza Zahedi ◽  
Hoda Mashayekhi ◽  
◽  
◽  
...  
Keyword(s):  
Topic Model ◽  
Model Based ◽  
Probabilistic Topic Model

A Probabilistic Topic Model based on Short Distance Co-occurrences

2022 ◽  
pp. 116518
Author(s):  
Marziea Rahimi ◽  
Morteza Zahedi ◽  
Hoda Mashayekhi
Keyword(s):  
Short Distance ◽  
Topic Model ◽  
Model Based ◽  
Probabilistic Topic Model

scholarly journals PF : Website Fingerprinting Attack Using Probabilistic Topic Model

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Hongcheng Zou ◽  
Ziling Wei ◽  
Jinshu Su ◽  
Baokang Zhao ◽  
Yusheng Xia ◽  
...  
Keyword(s):  
Topic Model ◽  
False Positive Rate ◽  
True Positive Rate ◽  
The Other ◽  
Open World ◽  
Closed World ◽  
Probabilistic Topic Model ◽  
High True Positive Rate ◽  
Positive Rate ◽  
Fingerprinting Attack

Website fingerprinting (WFP) attack enables identifying the websites a user is browsing even under the protection of privacy-enhancing technologies (PETs). Previous studies demonstrate that most machine-learning attacks need multiple types of features as input, thus inducing tremendous feature engineering work. However, we show the other alternative. That is, we present Probabilistic Fingerprinting (PF), a new website fingerprinting attack that merely leverages one type of features. They are produced by using a mathematical model PWFP that combines a probabilistic topic model with WFP for the first time, due to a finding that a plain text and the sequence file generated from a traffic instance are essentially the same. Experimental results show that the proposed new features are more distinguishing than the existing features. In a closed-world setting, PF attains a better accuracy performance (99.79% at most) than prior attacks on various datasets gathered in the scenarios of Shadowsocks, SSH, and TLS, respectively. Besides, even when the number of training instances drops to as few as 4, PF still reaches an accuracy of above 90%. In the more realistic open-world setting, PF attains a high true positive rate (TPR) and Bayes detection rate (BDR), and a low false positive rate (FPR) in all evaluations, which outperforms the other attacks. These results highlight that it is meaningful and possible to explore new features to improve the accuracy of WFP attacks.

Mobile Geographic Web Search Personalization with Language Model

2013 ◽  
Vol 303-306 ◽  
pp. 1420-1425
Author(s):  
Qiang Pu ◽  
Ahmed Lbath ◽  
Da Qing He
Keyword(s):  
Query Expansion ◽  
Web Search ◽  
Mixed Model ◽  
Topic Model ◽  
Language Model ◽  
Mobile Users ◽  
Information Need ◽  
User Interest ◽  
Modeling Framework ◽  
Model Based

Mobile personalized web search has been introduced for the purpose of distinguishing mobile user's personal different search interest. We first take the user's location information into account to do a geographic query expansion, then present an approach to personalizing web search for mobile users within language modeling framework. We estimate a user mixed model estimated according to both activated ontological topic model-based feedback and user interest model to re-rank the results from geographic query expansion. Experiments show that language model based re-ranking method is effective in presenting more relevant documents on the top retrieved results to mobile users. The main contribution of the improvements comes from the consideration of geographic information, ontological topic information and user interests together to find more relevant documents for satisfying their personal information need.

scholarly journals Author Topic Model based Collaborative Filtering for Personalized POI Recommendation

2015 ◽  
pp. 1-1 ◽  
Author(s):  
Shuhui Jiang ◽  
Xueming Qian ◽  
Jialie Shen ◽  
Yun Fu ◽  
Tao Mei
Keyword(s):  
Collaborative Filtering ◽  
Topic Model ◽  
Model Based ◽  
Poi Recommendation
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