Byungho Chung

Group signatures with controllable linkability for dynamic membership

In this paper we present a novel group signature scheme for dynamic membership which enables fine-grained control over the release of user information. This scheme could be widely used for various anonymity-based applications such as privacy-preserving data mining and customized anonymous authentication owing to a useful property called controllable linkability. A valid signer is able to create signatures that hide his or her identity as normal group signatures but can be anonymously linked regardless of changes to the membership status of the signer and without exposure of the history of the joining and revocation. From signatures, only linkage information can be disclosed, with a special linking key. Using this controllable linkability and the controllable anonymity of a group signature, anonymity may be flexibly or elaborately controlled according to a desired level. To begin construction of our scheme, we first introduce the Decision Linear Combination (DLC) assumption in a so-called gap Diffie-Hellman group where the DDH problem is tractable but the CDH problem is hard, and we prove that this assumption can be guaranteed in generic bilinear groups. To identify security requirements more precisely, we formally present definitions of anonymity, traceability, non-frameabilty, and linkability. We then prove that our scheme achieves all these security properties in the random oracle model. Our scheme supporting controllable linkability yields a short signature that is only 33.3% longer than the best-known normal group signature. Furthermore, we show that our scheme is comparable to the group signature scheme in terms of the amount of computation for basic operations such as signing, verification, and the key update caused by revocation. Finally, using the linkability for dynamic membership, computation overhead in opening signers identity can be significantly reduced or minimized.

Adult Image Detection Using Bayesian Decision Rule Weighted by SVM Probability

The SVM (support vector machine) and the SCM (skin color model) are used in detection of adult contents on images. The SVM consists of multi-class learning model and is very effective method for face detection, but complex. On the contrary, the SCM is very simple for detecting adult images using skin ratio derived from statistical characteristics of RGB color information, but less effective in close-up facial images. Hence, we propose a hybrid scheme that combines the SVM for the 1st filtering scheme using learning model (with classes of adult, benign and close-up facial images) with the SCM for the 2nd filtering scheme using skin ratio and adaptive MAP (maximum a posterior) hypothesis test based on Bayes’ theorem that improves the probability of true positive detection rate of adult images.

Anonymous Authentication System Using Group Signature

As the internet has been widely spread, it becomes big issues that an excessive demand of unnecessary private information at web servers and invasion of privacy. Anonymous authentication technology is a solution of this problem. It can protect the personal information and guarantees the convenience which an internet gives in our life. Anonymous authentication is an authentication method based on a digital signature scheme offering anonymity such as blind signature, group signature, ring signature, traceable signature, etc. The digital signature scheme that providing anonymity had been being mainly studied for an application including an e-cash and electronic voting system, etc. But recently the digital signature method is studied in the internet environment with one of the anonymous authentication method for the protecting personal information. In this paper, we introduce a group signature scheme which is one of the signature schemes supplying anonymity and propose the anonymous authentication system structure using the group signature scheme.

Classification and Detection of Objectionable Sounds Using Repeated Curve-Like Spectrum Feature

This paper proposes the repeated curve-like spectrum feature in order to classify and detect objectionable sounds. Objectionable sounds in this paper refer to the audio signals generated from sexual moans and screams in various sexual scenes. For reasonable results, we define the audio-based objectionable conceptual model with six categories from which dataset of objectionable classes are constructed. The support vector machine classifier is used for training and classifying dataset. The proposed feature set has accurate rate, precision, and recall at about 96%, 96%, and 90% respectively. With these measured performance, this paper shows that the repeated curve-like spectrum feature proposed in this paper can be a proper feature to detect and classify objectionable multimedia contents.

Classifying of Objectionable Contents with Various Audio Signal Features

The rapid development of multimedia related technologies and internet infrastructure have made general users can create, edit, and post their contents and can easily access any content that they desire. But it also leads to the harmful side effects that are creation and uncontrolled distribution of objectionable contents. Especially it is very serious for pornographic contents that are more than about 70% of objectionable contents. The objectionable contents mean the pornographic contents in this paper. Most of the related studies are focused on image-based approaches and there are few studies based on audio-based approaches. In this paper, we try to classify objectionable contents based on various audio signal features. The audio signal features used in this paper are perceptual features that are spectral properties, MFCC based feature set and TDMFCC based feature set. For the reasonable results, we define the audio-based objectionable contents model and then construct dataset according to the defined model. For training and classifying dataset of two classes, objectionable and nonobjectionable class, SVM classifier is used. TDMFCC based feature set has a good performance of accurate rate with SVM classifier, about 95%, and the results show that it is very effective to detect and classify the objectionable contents based on audio features.

Detection of Rogue Devices in WLAN by Analyzing RF Features and Indoor Location of the Device

In this paper, we present rogue device detection mechanism in WLAN Wireless Local Area Network by analyzing radio frequency RF features and estimating indoor location of the device. The presented mechanism analyzes error vector magnitude EVM as a RF feature and it also utilizes indoor location to improve detection rates. To estimate location, we use the triangulation method with Gauss---Seidel iterative technique to find approximate coordinate. We developed the proposed mechanism in the wireless sensor hardware and wireless intrusion prevention server platform, and we provide experimental results.

Analysis and Countermeasure on Vulnerability of WPA Key Exchange Mechanism

In this paper, we analyze some weaknesses in WPA authenticator key management state machine and propose the countermeasures to overcome these problems. Our researches on IEEE 802.11i authenticator state machine that is WPA authenticator key management state machine reveal that the state machine cannot support the stable group key setting and is vulnerable to the replay attack and DoS attack. We describe 3 problems related to these vulnerabilities, propose the respective solutions and reconstruct WPA authenticator key management state machine to which the alternative solutions are applied.

Design and Implementation of Security Threat Detection and Access Control System for Connected Car

Security vulnerabilities are also increasing as connectivity increases to provide driving stability and convenience for automobiles. In this paper, we design a white list-based access control system to detect and block malicious attempts to access an in-vehicle network through an infotainment device in a connected car environment, and present the implementation results.

The Vessel Trajectory Mechanism for Marine Accidents Analysis

In this paper, we provide a mechanism to be able to save time and human resources to predict the time of occurrence of accidents at sea this time of the incident are not clear and to extract the suspected vessel-related accidents at sea. The proposed mechanism, it save such amount of data and time to trajectory extraction by managing separate the control area in a grid pattern, and is characterized as possible through trajectory analysis for a particular area. It also can be reduced 30 times faster than existing marine accident analysis time using the playback function in VTS operating system, so it is effective in saving human resource and time.

Anonymous Access Control Framework Based on Group Signature

Anonymous authentication is a method for privacy protection in web service. But, the nature of anonymous authentication, access control of service is seemed impossible because it doesn???t know the user???s characteristics. In our paper, we propose an anonymous authorization framework which uses qualification certificate and anonymous authentication based on Short Group Signatures. Also, our qualification certificate is used as payment token. By using our proposed authorization protocol, users can use anonymous authentication, anonymous authorization, and anonymous payment in web service.