Browsing by Author "Chen, Yi-Ruei"

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A Public-Key Traitor Tracing Scheme with an Optimal Transmission Rate
(2009)
Chen, Yi-Ruei
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Tzeng, Wen-Guey
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資訊工程學系
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Department of Computer Science
The way of transmitting the encrypted digital content, to the legitimate subscribers over a broadcast channel has wide commercial applications; such as Pay-TV, DVD, etc In order to discourage the legitimate subscribers from giving away their decryption keys, the traitor tracing scheme collies up In this paper, we propose a public-key traitor tracing scheme that has optimal transmission rate In other words, our scheme enables everyone to transmit the encrypted digital contents almost, without, any redundancy As for tracing, our scheme supports black-box tracing, i.e., identifying colluders without opening the pirate decode Moreover, in our scheme, the storage requirement, for legitimate subscribers and digital content broadcasters is smaller than that of previous schemes
An Online Subject-Based Spam Filter Using Natural Language Features
(2017-01-01)
Lee, Chih-Ning
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Chen, Yi-Ruei
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Tzeng, Wen-Guey
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資訊工程學系
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Department of Computer Science
This paper proposes an online subject-based spam filter built upon an extended version of weighted naive Bayesian (WNB) classifier. The spam filter checks email subjects only. It is faster than spam filters that scan whole body of emails and useful even spam senders temper email bodies to avoid filtering. In addition to the widely used bag-of-word feature, we further consider statistical and nature language features to discover new characteristics from email subjects. In online learning, we use an extended WNB classifier. It is not only computationally efficient, but also more adaptive to the changes of spams with new malicious campaigns. The proposed classifier is immune to the spams with malicious campaigns beyond contemplation. We evaluate the performance of our spam filter on 8 well-known ham-spam email datasets from TREC and Enron-Spam corpus. Our approach achieves 94.85% of accuracy and 95.8% of F1-measure on TREC datasets, and 95.74% of accuracy and 97.2% of F1-measure on Enron-Spam datasets. Compared with previous works of the same line, our approach has 2.43%, 2.3%, and 3.2% improvements on accuracy, true positive rate, and false positive rate, respectively.
Effective Botnet Detection Through Neural Networks on Convolutional Features
(2018-01-01)
Chen, Shao-Chien
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Chen, Yi-Ruei
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Tzeng, Wen-Guey
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資訊工程學系
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Department of Computer Science
Botnet is one of the major threats on the Internet for committing cybercrimes, such as DDoS attacks, stealing sensitive information, spreading spams, etc. It is a challenging issue to detect modern botnets that are continuously improving for evading detection. In this paper, we propose a machine learning based botnet detection system that is shown to be effective in identifying P2P botnets. Our approach extracts convolutional version of effective flow-based features, and trains a classification model by using a feed-forward artificial neural network. The experimental results show that the accuracy of detection using the convolutional features is better than the ones using the traditional features. It can achieve 94.7% of detection accuracy and 2.2% of false positive rate on the known P2P botnet datasets. Furthermore, our system provides an additional confidence testing for enhancing performance of botnet detection. It further classifies the network traffic of insufficient confidence in the neural network. The experiment shows that this stage can increase the detection accuracy up to 98.6% and decrease the false positive rate up to 0.5%.
Group key management with efficient rekey mechanism: A Semi-Stateful approach for out-of-Synchronized members
(2017-01-15)
Chen, Yi-Ruei
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Tzeng, Wen-Guey
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資訊工程學系
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Department of Computer Science
This paper addresses the problem of managing a cryptographic group key among a large and highly dynamic group of members, who may miss group key update (rekey) messages frequently. We propose two provably-secure and practical schemes: KeyDer-GKM and ReEnc-GKM. The rekey process in these schemes has an O(logN) rekey message and O(logN) computation and storage cost for a member, where N is the number of group members. Moreover, our schemes have the following distinct features. (1) Each member is given only one private key and O(logN) public information. The private key remains unchanged during the membership period. For the public information, a member can hold them locally and update accordingly from each rekey message, or get them from a public bulletin if needed. (2) The size of published information is O(N) no matter how many rekey processes occur. The computation cost for a member, who has missed some rekey messages, to compute the up-to-date group key is always O(logN) no matter how many rekey messages have been missed. Our KeyDer-GKM scheme is very efficient since it can be implemented by using hash and XOR functions only. Our ReEnc-GKM scheme allows a member to reduce the cost of computing the up-to-date group key to one decryption by outsourcing logN operations. Both of our schemes are shown immune to the collusion attacks. For KeyDer-GKM, a set of collusive members cannot recover an unauthorized group key. For ReEnc-GKM, a set of collusive members cannot distinguish an unauthorized group key from a random string. (C) 2016 Elsevier B.V. All rights reserved.
Privacy-preserving ridge regression on distributed data
(2018-07-01)
Chen, Yi-Ruei
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Rezapour, Amir
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Tzeng, Wen-Guey
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交大名義發表
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National Chiao Tung University
Ridge regression is a statistical method for modeling a linear relationship between a dependent variable and some explanatory values. It is a building-block that plays a major role in many learning algorithms such as recommendation systems. However, in many applications such as e-health, explanatory values contains private information owned by different patients that are not willing to share them, unless data privacy is guaranteed. In this paper, we propose a protocol for conducting privacy-preserving ridge regression (PPRR) over high-dimensional data. In our protocol, each user submits its data in an encrypted form to an evaluator and the evaluator computes a linear model of all users' data without learning their contents. The core encryption method is equipped with homomorphic properties to enable the evaluator to perform ridge regression over encrypted data. We implement our protocol and demonstrate that it is suitable for dealing with high-dimensional data distributed among millions of users. We also compare our protocol with the state-of-the-art solutions in terms of both computation and communication costs. The results show that our protocol outperforms most existing approaches based on secure multi-party computation, garbled circuit, fully homomorphic encryption, secret-sharing, and hybrid methods. (C) 2018 Elsevier Inc. All rights reserved.
Secure Group Key Management Using Uni-Directional Proxy Re-Encryption Schemes
(2011)
Chen, Yi-Ruei
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Tygar, J. D.
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Tzeng, Wen-Guey
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資訊工程學系
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Department of Computer Science
The group key management is for a group manager to maintain a consistent group key for a dynamic group of members through a broadcast channel. In this paper we propose a group key management scheme based on a meta proxy re-encryption (PRE) scheme. In particular, we propose an RSA-based PRE scheme with special properties. It is the first RSA-based PRE scheme for group key management and has the desired properties of uni-directionality and multi-hop. In our group key management scheme, each group member holds just one secret auxiliary key and log N public auxiliary keys. The size of rekey messages for each group key update remains O(log N). Additionally, our scheme has some distinct features. Firstly, the size of the key update history is a constant O(N) no matter how many times of group key updates occur. Secondly, the computation time of computing the newest group key from the key update history is always O(log N) no matter how many group key updates are missed. This feature provides a practical solution for group key update when members go off-line from time to time. Finally, the proposed scheme is immune to the collusion attack of other members.
Weave ElGamal Encryption for Secure Outsourcing Algebraic Computations Over Z(p)
(2017-01)
Chen, Yi-Ruei
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Shen, Shiuan-Tzuo
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Tzeng, Wen-Guey
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資訊工程學系
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Department of Computer Science
This paper addresses the secure outsourcing problem for large-scale matrix computation to a public cloud. We propose a novel public-key weave ElGamal encryption (WEE) scheme for encrypting a matrix over the field Z(p). The scheme has the echelon transformation property. We can apply a series of elementary row/column operations to transform an encrypted matrix under our WEE scheme into the row/column echelon form. The decrypted result matches the result of the corresponding operations performed on the original matrix. For security, our WEE scheme is shown to be entry irrecoverable for non-zero entries under the computational Diffie-Hellman assumption. By using our WEE scheme, we propose five secure outsourcing protocols of Gaussian elimination, Gaussian-Jordan elimination, matrix determinant, linear system solver, and matrix inversion. Each of these protocols preserves data privacy for clients (data owners). Furthermore, the linear system solver and matrix inversion protocols provide a cheating-resistant mechanism to verify correctness of computation results. Our experimental result shows that our protocols gain efficiency significantly for an outsourcer. Our outsourcing protocol solves a linear system of n = 1,000 equations and in = 1,000 unknown variables about 472 times faster than a non-outsourced version. The efficiency gain is more substantial when (n, m) gets larger. For example, when n = 10,000 and m = 10,000, the protocol can solve it about 56,274 times faster. Our protocols can also be easily implemented in parallel computation architecture to get more efficiency improvement.