Jinyoung Moon

JavaCard-based Two-Level User Key Management for IP Conditional Access Systems

To prohibit the unauthorized accesses on premium contents of the Pay-TV system, a conditional access system (CAS) scrambles the contents with a scrambling key. In order to securely send the scrambling key for descrambling, the CAS transmits the scrambling key after encrypting it with another encryption key. The encryption keys are generally transmitted by entitlement management message (EMM). In this paper, we propose a two-level user key management scheme for the IP CAS used in the IPTV system. A user key for every subscriber is subdivided into permanent subscriber key (SK) and updatable user key (UK). The SK is used to obtain the UK by an authentication protocol. According to the authentication protocol, the authentication server requests the results of cryptographic operations to the authentication client to verify the authentication client. The UK is used to encrypt the next encryption key. The proposed scheme strengthens the security of the IP CAS because the use of the UK for the EMMs enables the lifetime of the SK to be infinite and the replacement of the smartcard to be eliminated. In addition, the IP CAS adopts Java card instead of Smart card because Java card applets are able to be dynamically reloaded.

A Dynamic Conditional Access System for IPTV Multimedia Systems

An IPTV system that transmits contents through anIP network needs to adopt a conditional access system allowingonly authorized subscribers to view premium content as inother Pay-TV systems. IPTV systems adopting one ofconditional access systems provided by a specific major vendorhave a difficulty in changing the conditional access systemloaded on a set-top box or on a removable hardware device,such as Smart card. However, a software download schemethrough an IP network enables an IPTV set-top box to changethe existing conditional access system in a secure way. Thispaper proposes a dynamic conditional access system for anIPTV set-top box that securely downloads cryptographicsoftware and dynamically executes the cryptographic softwarewhen it is required. Therefore, the proposed system enables anIPTV multimedia system to provide seamless conditionalaccess services to IPTV subscribers without rebooting theIPTV set-top box or replacing a hardware security devicewhile changing the running cryptographic software.

Achieving Interoperability in Conditional Access Systems through the Dynamic Download and Execution of Cryptographic Software for the IPTV System

An IPTV system that transmits multimedia contents through the IP network needs to adopt a conditional access system allowing the only entitled subscribers to view the premium content as other pay-TV systems do. Although several standard specifications have been published for interoperable models of conditional access systems, the proposed models are based on the unidirectional or restricted bidirectional broadcasting network and do not utilize the characteristics of the IP network. However, software download scheme through the bidirectional IP network enables it to change existing conditional access systems in a secure way. This paper introduces an interoperable conditional access system that can update any cryptographic software provided by different conditional access system vendors through downloading and executing dynamically the downloaded conditional access software only if the software follows the pre-defined interfaces.

Provision of the Social Media Service Framework based on the locality/sociality relations

Although the conventional Social Network Services (SNS) provides a solution for internetworking social users to share information and social media contents based on the World Wide Web (WWW), there are additional requirements to support increasing demands of social users with commencing Web 2.0 and Semantic Web technologies. The paper introduces a methodology to provide the Social Media Service Framework (SMSF) in order to overcome the limitations of the traditional SNS services and to support the fundamental semantic web technologies including locality and sociality relationship management, active information and knowledge sharing schemes, digital community management, social user management based on ontology system. Based on the proposed service framework, the paper implements a method to provide a personalized SNS service based on the locality and sociality relations with user relationship, autonomous information and knowledge management based on the ontology schemes. To do so, the paper initially proposes a social messenger and relationship management system for communication and active information sharing respectively.

Hierarchically linked infinite hidden Markov model based trajectory analysis and semantic region retrieval in a trajectory dataset

A novel model for trajectories and semantic regions (sest-hiHMM) is proposed.A sticky version of sest-hiHMMs is proposed for reducing redundant semantic regions.An extended definition of semantic regions covers actual regions, not sets of points.Our models concern the temporal dependency of observations in a trajectory.Our models retrieve reasonable semantic regions from a real trajectory dataset. With an increasing attempt of finding latent semantics in a video dataset, trajectories have become key components since they intrinsically include concise characteristics of object movements. An approach to analyze a trajectory dataset has concentrated on semantic region retrieval, which extracts some regions in which have their own patterns of object movements. Semantic region retrieval has become an important topic since the semantic regions are useful for various applications, such as activity analysis. The previous literatures, however, have just revealed semantically relevant points, rather than actual regions, and have less consideration of temporal dependency of observations in a trajectory. In this paper, we propose a novel model for trajectory analysis and semantic region retrieval. We first extend the meaning of semantic regions that can cover actual regions. We build a model for the extended semantic regions based on a hierarchically linked infinite hidden Markov model, which can capture the temporal dependency between adjacent observations, and retrieve the semantic regions from a trajectory dataset. In addition, we propose a sticky extension to diminish redundant semantic regions that occur in a non-sticky model. The experimental results demonstrate that our models well extract semantic regions from a real trajectory dataset.

Trajectory Segmentation Based on Spatio-Temporal Locality with Multidimensional Index Structures

Despite the remarkable growth of video analysis technologies, human operators still suffer from the difficulties of careful monitoring of a lot of videos in many industrial applications. Since a number of methods for understanding videos usually consider object movements, it is also concentrated on trajectory analysis. Due to the high and variable dimensionality of trajectories, trajectory analysis is not trivial. Some studies divided each trajectory into several pieces. However, the lack of discussions on how to segment concerning trajectory analysis led to flood too naive or too complicated methods. In this paper, we propose a simple but effective method of trajectory segmentation concerning spaito-temporal locality. Using multidimensional index structures and some temporal concerns, a great set of trajectory segments can be constructed in a short time. In addition, we extracted semantic regions, as an example of trajectory analysis, with the results of trajectory segmentation. The experiments showed that trajectory segments reflect on the spatio-temporal locality, and semantic regions were well extracted, which indicated that our segmentation had potential for trajectory analysis.

Knowledge Digest Engine for Personal Bigdata Analysis

The bigdata analysis has an issue of high knowledge creation. In this paper first, we define personal big data, and using personal bigdata created by user activity we try to create high knowledge about the user. We have created personal bigdata analytic engine and knowledge digest engine for high knowledge creation and personalized service. The engine is used to collect, process and analyize personal big data. And In the process we refine, associate, and fuse data for analysis. In this paper, we show the process of analyzing personal big data, and detailed structure of analyzing engine for persoanl big data. High knowledge about the user will lead to better personalized services, and better adaptive services.

A dynamic conditional access system based on cryptographic software for the IPTV set-top box

This paper proposes a dynamic conditional access system for the IPTV set-top box. The conditional access system dynamically downloads and executes cryptographic software for obtaining control words used for descrambling. The proposed system enables the IPTV set-top box to provide seamless conditional access services to IPTV subscribers without rebooting the set-top box or replacing a hardware security device while the running cryptographic software is changing. Therefore, the IPTV set-top box applying the proposed system also can provide channels that adopt different conditional access softwares.

Detecting user attention to video segments using interval EEG features

Abstract To manage voluminous viewed videos, which US adults watch at a rate of more than five hours per day on average, an automatic method of detecting highly attended video segments during video viewing is required to access them for fine-grained sharing and rewatching. Most electroencephalography (EEG)-based studies of user state analysis have addressed the recognition of attention-related states in a specific task condition, such as drowsiness during driving, attention during learning, and mental fatigue during task execution. In contrast to attention in a specific task condition, both inattention and normal attention are meaningless to viewers in terms of managing viewed videos, while detecting high attention paid to video segments would make a valuable contribution to an automatic management system of viewed videos based on viewer attention. To the best of our knowledge, this is the first EEG-based study of detecting viewer attention paid to video segments. This study describes how to collect video-induced EEG and attention data for video segments from viewers without bias to specific genres and how to construct a subject-independent detection model for the top 20% of viewer attention. The attention detection model using the proposed interval EEG features from 14 channels achieved the best average F 1 score of 39.79% with an average accuracy of 52.96%. Additionally, this paper proposes a channel-based feature selection method that considers both the performances of single-channel models and their physical locations for investigating the group of channels relevant to attention detection. The attention detection models using the interval EEG features from all four or some of the channels located in the fronto-central, parietal, temporal, and occipital lobes of the left hemisphere achieved the best F 1 score of 39.60% with an average accuracy of 48.70%. It is shown that these models achieve better performance than models using the features from all four or some of their symmetric channels in the right hemisphere and models using the features from six channels located in the anterior-frontal and frontal lobes of the left and right hemispheres. This paper shows the feasibility of subject-independent and genre-independent attention detection models using a wireless EEG headset with optimized channels; these models can be applied to an intelligent video management system based on viewer attention in real-world scenarios.

A Knowledge-Driven Approach to Interactive Event Recognition for Semantic Video Understanding

Since early 1990, event recognition has been one of the most attractive research topics for video understanding, in company with object recognition. Most studies on video event recognition, which are based on data-driven approaches, should train a model for a newly-added event without using human knowledge and existing models for similar events. Because it is impossible to define all events required for video understanding in advance, this paper proposed a hierarchical recognition method for general events based on dynamic spatial relations between two objects and specialized events determined by the related objects. The general events are useful for describing interactions between objects of interest regardless of video domain. The specialized events can be provided to users as familiar terms in video interpretation or visual question answering for user-friendly interaction. For two general events and their specialized four events, the proposed recognition method performed the F-score of 82.31% and 88.61% based on object-based and region-based event matching, respectively.