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How did you know that about me? Protecting users against unwanted inferences

Sara Motahari, Julia Mayer, Quentin Jones
2011 EAI Endorsed Transactions on Security and Safety  
A social inference risk prediction framework is presented and associated empirical studies that attest to its validity.  ...  This framework is then used to outline the major research and practical challenges that need to be addressed if we are to deploy effective social inference protection systems.  ...  As a result, systems cannot protect users from social inferences by applying traditional database inference protection techniques. An alternative way is to use an entropy-based approach.  ... 
doi:10.4108/trans.sesa.2011.e3 fatcat:mtjjpssykzf3vlxs76s75z66ny

Social Inference Risk Modeling in Mobile and Social Applications

Sara Motahari, Sotirios Ziavras, Mor Naaman, Mohamed Ismail, Quentin Jones
2009 2009 International Conference on Computational Science and Engineering  
In this paper, after analyzing the social inference problem theoretically, we assess the extent of the risk to users of computer-mediated communication and location based applications through 1) a laboratory  ...  The work validates the theoretical model, highlights the seriousness of the social inference risk, and shows how the extent and nature of the risk differs for different classes of social computing applications  ...  We will also provide a framework for modeling users' background knowledge so that we can calculate information entropy and predict the risk of social inferences.  ... 
doi:10.1109/cse.2009.237 dblp:conf/cse/MotahariZNIJ09 fatcat:4qm5nv7zmrap5hgagormtd5nbu

Preventing Unwanted Social Inferences with Classification Tree Analysis

Sara Motahari, Sotirios Ziavras, Quentin Jones
2009 2009 21st IEEE International Conference on Tools with Artificial Intelligence  
We have previously introduced 'inference functions' to estimate the social inference risk based on information entropy.  ...  This is mostly due to the lack of enough training data for high-risk, low-entropy situations and outliers.  ...  Prediction and Classification of High Risk Situations In this section we review the social inference problem, the relation between social inferences and information entropy, and our entropy-based framework  ... 
doi:10.1109/ictai.2009.15 dblp:conf/ictai/MotahariZJ09 fatcat:drluetbbofefhbje3ommxmi7vq

Detection Block Model for SQL Injection Attacks

Diksha G. Kumar, Madhumita Chatterjee
2014 International Journal of Computer Network and Information Security  
With the rapid development of Internet, more and more organizations connect their databases to the Internet for resource sharing.  ...  An attacker can directly compromise the database, and that is why this is a most threatening web attack.  ...  There are two well known attack techniques that are based on inference. They allow an attacker to extract data from a database and detect vulnerable parameters.  ... 
doi:10.5815/ijcnis.2014.11.08 fatcat:qtdocu6l2ncebk6c67yrw27e6a

Page 88 of Computational Linguistics Vol. 14, Issue 2 [page]

1988 Computational Linguistics  
For use in patten-directed inference systems, or rule-based inference engines, artificial intelligence researchers have favored others largely for reasons of simplicity and speed.  ...  This thesis also examines the question of choices of semantics for update operators for databases with incomplete information, and proposes a framework for evaluation of competing candidate semantics.  ... 

Query-Driven Approach to Face Clustering and Tagging

Liyan Zhang, Xikui Wang, Dmitri V. Kalashnikov, Sharad Mehrotra, Deva Ramanan
2016 IEEE Transactions on Image Processing  
We use a data-driven Gaussian process model of facial appearance to write the probabilistic estimates of facial identity into a probabilistic database, which can then support inference through query answering  ...  We integrate active learning with query-driven probabilistic databases.  ...  For example, Siddiquie and Gupta [20] present an active learning framework to simultaneously learn appearance and contextual models for multi-class classification.  ... 
doi:10.1109/tip.2016.2592703 pmid:27448352 fatcat:b7jnbgq7rvg6powqnq6f5ybxjq

An Information-Theoretic Approach to Inference Attacks on Random Data Perturbation and a Related Privacy Measure

P.L. Vora
2007 IEEE Transactions on Information Theory  
This correspondence provides an information-theoretic framework for all inference attacks on RDP.  ...  The framework is used to demonstrate the existence of a tight asymptotic lower bound on the number of queries required per bit of entropy for all inference attacks with zero asymptotic error and bounded  ...  s, such as in an inference attack. This correspondence examines the most general inference attacks, and their costs, in terms of the number of queries required per bit of entropy.  ... 
doi:10.1109/tit.2007.901183 fatcat:vk46x2wrz5fvjilwx5kg6bdt3u

Utilizing RNA-Seq data for cancer network inference

Ying Cai, Bernard Fendler, Gurinder S. Atwal
2012 Proceedings 2012 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS)  
However, there are onerous statistical and computational issues in interpreting high-dimensional sequencing data and inferring the underlying genetic network.  ...  An important challenge in cancer systems biology is to uncover the complex network of interactions between genes (tumor suppressor genes and oncogenes) implicated in cancer.  ...  To prevent overfitting we can provide an estimate of the sparse inverse covariance matrix using the graphical lasso framework [6] .  ... 
doi:10.1109/gensips.2012.6507723 dblp:conf/gensips/CaiFA12 fatcat:c5fse46f3bagnhwtudnnavweam

Utilizing Shannon's Entropy to Create Privacy Aware Architectures [article]

Abhinav Palia, Rajat Tandon, Carl Mathis
2021 arXiv   pre-print
In this paper, we utilize Shannon's Entropy to create an objective metric that can help simplify the state-of-the-art Privacy Design Strategies proposed in the literature and aid our key technical design  ...  Individual consent and transparency are the core tenets for earning customers trust and this motivates the organizations to adopt privacy enhancing practices while creating the systems.  ...  Data Abstraction: Based on our formulation, adding similar records (same values for quasi-identifiers) in the database or perturbing the database decreases the probability of identifying an individual.  ... 
arXiv:2109.04649v1 fatcat:kqlygc6ozvevzgokhmxk6xztky

An Integrated Framework for Database Privacy Protection [chapter]

LiWu Chang, Ira S. Moskowitz
2002 IFIP International Federation for Information Processing  
The framework is based on an association network which is composed of a similarity measure and a Bayesian network model.  ...  In this paper, we present a framework to assist in the formal analysis of the database inference problem.  ...  Acknowledgements We thank the anonymous reviewers for their helpful comments and suggestions.  ... 
doi:10.1007/0-306-47008-x_15 fatcat:xiqemwxlt5ba3c2qebdrap5u4u

Online Anonymity Protection in Computer-Mediated Communication

Sara Motahari, Sotirios G. Ziavras, Quentin Jones
2010 IEEE Transactions on Information Forensics and Security  
We present an information-entropy-based realistic estimation of the user anonymity level to deal with these issues in social computing in an effort to help predict \identity inference risks.  ...  We then address implementation issues of online protection by proposing complexity reduction methods that take advantage of basic information entropy properties.  ...  A social inference risk prediction framework based on the information entropy of a specific user attribute was proposed in [15, 16] . The contributions of this paper are as follows.  ... 
doi:10.1109/tifs.2010.2051261 fatcat:iot56tifznex7kepjft6ya2upi

Biomarker Prioritisation and Power Estimation Using Ensemble Gene Regulatory Network Inference

Furqan Aziz, Animesh Acharjee, John A. Williams, Dominic Russ, Laura Bravo-Merodio, Georgios V. Gkoutos
2020 International Journal of Molecular Sciences  
In this study, we used two methods, namely the MIDER (Mutual Information Distance and Entropy Reduction) and the PLSNET (Partial least square based feature selection) methods, to infer the structure of  ...  We further demonstrate that an ensemble-based approach, that combines the output of the MIDER and PLSNET algorithms, can infer the structure of a GRN from data with higher accuracy.  ...  An entropy reduction, based on conditional entropies, is then applied to further refine the map. This allows for the discriminating between direct and indirect connections.  ... 
doi:10.3390/ijms21217886 pmid:33114263 pmcid:PMC7660606 fatcat:srk7tskw4jeqfnxcf34iljmzjm

Using Entropy Leads to a Better Understanding of Biological Systems

Chih-Yuan Tseng, Jack A. Tuszynski
2011 Biophysical Journal  
We argue that a comprehensive approach that integrates laws of physics and principles of inference provides a better conceptual framework than these approaches.  ...  Users will have access to an in-depth analysis of each sequence and be allowed to analyze sequences not included in the database using the same prediction tools.  ...  We argue that a comprehensive approach that integrates laws of physics and principles of inference provides a better conceptual framework than these approaches.  ... 
doi:10.1016/j.bpj.2010.12.1949 fatcat:j5a6vt7thvb55levrzrprmjyey

Utilizing RNA-Seq Data for Cancer Network Inference [article]

Ying Cai, Bernard Fendler, Gurinder S. Atwal
2012 arXiv   pre-print
However, there are onerous statistical and computational issues in interpreting high-dimensional sequencing data and inferring the underlying genetic network.  ...  An important challenge in cancer systems biology is to uncover the complex network of interactions between genes (tumor suppressor genes and oncogenes) implicated in cancer.  ...  To prevent overfitting we can provide an estimate of the sparse inverse covariance matrix using the graphical lasso framework [6] .  ... 
arXiv:1211.4543v2 fatcat:h6ynxm6whjgcvnjk462zegac5a

Identity Inference as a Privacy Risk in Computer-Mediated Communication

Sara Motahari, Sotirios G. Ziavras, Richard P. Schuler, Quentin Jones
2009 2009 42nd Hawaii International Conference on System Sciences  
the relation between information entropy and social inference.  ...  An important, yet under-researched privacy risk results from social inferences about user identity, location, and activities. In this paper, we frame the 'social inference problem'.  ...  Calculating the Entropy for Instantaneous Inferences (Category 5) As mentioned in Section 3, Morgenstern [8, 22] formulated for the first time partial inferences based on the entropy of information,  ... 
doi:10.1109/hicss.2009.243 dblp:conf/hicss/MotahariZSJ09 fatcat:6anjhpg26zfu7dfnnmw63avspu
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