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Time-related information in text posted on-line is one type of personal information targeted by attackers, one reason that sharing information online can be risky. Therefore, time information should be anonymized before it is posted on social networking services. One approach to anonymizing information is to replace sensitive phrases with anonymous phrases, but attackers can usually spot such anonymization due to its unnaturalness. Another approach is to detect temporal passages in the text, but removal of these passages can make the meaning of the text unnatural. We have developed an algorithm that can be used to anonymize time-related personal information by removing the temporal passages when doing so will not change the natural meaning of the message. The temporal phrases are detected by using machine-learned patterns, which are represented by a subtree of the sentence parsing tree. The temporal phrases in the parsing tree are distinguished from other parts of the tree by using temporal taggers integrated into the algorithm. In an experiment with 4008 sentences posted on a social network, 84.53 % of them were anonymized without changing their intended meaning. This is significantly better than the 72.88 % rate of the best previous temporal phrase detection algorithm. Of the learned patterns, the top ten most common ones were used to detect 87.78 % the temporal phrases. This means that only some of the most common patterns can be used to the anonymize temporal phrases in most messages to be posted on an SNS. The algorithm works well not only for temporal phrases in text posted on social networks but also for other types of phrases (such as location and objective ones), other areas (religion, politics, military, etc.), and other languages.

Password is one of the simple and efficient methods to protect the transactions in insecure network environments. There are many authors researching in this area to suggest the protocols preventing illegitimate users from accessing the systems. In 2013, Y-H An proposed the scheme to isolate some problems which exist in Khan et al.’s scheme. In this paper, we demonstrate that Y-H An’s scheme is vulnerable to server forgery attack and cannot provide user’s anonymity. Furthermore, we also propose the modified scheme to overcome these limitations.

The application of fingerprinting techniques to relational data cannot protect personal information against a collusion attack, in which the attacker has access to a set of published data. The general fingerprinting techniques such as Li et at.’s, Guo et al.’s, and Schrittwieser et al.’s focus on detecting the traitor, who leaked the data. Among them, Schrittwieser et al.’s fingerprinting technique combines \(k\)-anonymity and full-domain generalization in order to not only detect traitors but also protect personal records. However, the technique has two main limitations. First, it does not allow the data provider to insert or delete records from the original data. Secondly, it does not create enough fingerprints for data recipients. To overcome these limitations, in this paper, we propose an (\(\alpha ,k\))-privacy protection model, an extension of \(m\)-invariance and (\(\alpha , k\))-anonymity, and a new top-down (\(\alpha , k\))-privacy fingerprinting algorithm based on that model. The model not only protects sensitive personal information against collusion attacks but also allows data providers to republish their updated original data without degrading the privacy protection. The algorithm embeds fingerprints in the generalized data and extracts them from leaked data to detect the traitors. We extensively evaluate the proposed algorithm on our own built software. The evaluation results show that our algorithm creates more fingerprints than Schrittwieser et al.’s algorithm (64000 vs 1536) while achieving the same generalized data quality. Moreover, our (\(\alpha , k\))-privacy algorithm creates generalized data even in the case of having small number of distinct sensitive values in the original data without adding faked records as in \(m\)-invariance.

The authors propose a system that allows presenters to control presentations in a natural way by their body gestures and vocal commands. Thus a presentation no longer follows strictly a rigid sequential structure but can be delivered in various flexible and content adapted scenarios. Our proposed system fuses three interaction modules: gesture recognition with Kinect 3D skeletal data, key concepts detection by context analysis from natural speech, and small-scaled hand gesture recognition with haptic data from smart phone sensors. Each module can process in realtime with the accuracy of 95.0%, 91.2%, and 90.1% respectively. The system uses events generated from the three modules to trigger pre-defined scenarios in a presentation to enhance the exciting experience for audiences.

The authors propose a table-top with virtual keyboards for multi-users to work in a collaborative environment. The proposed system has two main modules: a system for virtual keyboards with touch event detection from depth data of a Kinect and a word predicting module based on the idea of Hidden Markov Model and Trie data structure. The system can replace physical keyboards, improve the accuracy of a virtual keyboard, and increase the typing speed of users. Our experimental results show that our system archives an accuracy of 94.416% with the virtual keyboard, saves 11-22% of keystrokes, and corrects 89.02% of typing mistakes.

Dynamic ID based authentication scheme is more and more important in wireless environments such as GSM, CDPD, 3G and 4G. One of important properties of such authentication scheme is anonymity. It must be guaranteed to defend the privacy of mobile users against outside attacks, and the scheme of Cheng-Chi Lee, Tsung-Hung Lin and Rui-Xiang Chang satisfies that requirement. However, another important property that should be considered is impersonation. The scheme must have capability to resist this kind of attack to protect legal users from illegal adversaries. In this paper, we demonstrate that Lee et al.’s scheme is still vulnerable to masquerade attack and session key attack with stolen smart card. Then we present an improvement of their scheme in order to isolate such problems.

Social networking services (SNSs) support communication among people via the Internet. However, sensitive information about a user can be disclosed by the user's SNS friends. This makes it unsafe for a user to share information with friends in different groups. Moreover, a friend who has disclosed a user's information is difficult to identify. One approach to overcoming this problem is to anonymize the sensitive information in text to be posted by generalization, but most methods proposed for this approach are for information in a database. Another approach is to create different fingerprints for certain sensitive information by using various synonyms. However, the methods proposed for doing this do not anonymize the information. We have developed an algorithm for automatically creating enough anonymous fingerprints to cover most cases of SNSs containing sensitive phrases. The fingerprints are created using both generalization and synonymization. A different fingerprinted version of sensitive information is created for each friend that will receive the posted text. The fingerprints not only anonymize a user's sensitive information but also can be used to identify a person who has disclosed sensitive information about the user. Fingerprints are quantified using a modified discernability metric to ensure that an appropriate level of privacy is used for each group to receive the posted text. The use of synonyms ensures that an appropriate level of privacy is used for each group to receive the posted text. Moreover, a fingerprint cannot be converted by an attacker into one that causes the algorithm to incorrectly identify a person who has revealed sensitive information. The algorithm was demonstrated by using it in an application for controlling the disclosure of information on Facebook.

The Web of Data is based on two simple ideas: to employ the RDF data model to public structured data on the Web and to set explicit RDF links to interlink data items within different data sources. In this paper, we describe our experience in building a system of link discovery between KAKEN, a database provides the latest information of research projects in Japan, and the DBLP Computer Science Bibliography. Using these links one can navigate from the information of a computer scientist in KAKEN to his publications in the DBLP database. Our problem of linkage between KAKE researchers and DBLP authors is name disambiguation. We proposed combining LDA based topic model and co-author network approach to improve linkage accuracy.

Standard supervised learning approaches apply the decision model to the new document to be classified in a “context-free” manner. To exploit the potential information in the relationship between the items, there are link-based methods which model the relationship as a graph and exploit further information for classification from the neighbourhood. These link-based methods learn from the collective information of the whole neighbourhood. Rather than taking the whole neighbourhood into consideration, the author proposes a model to calculate link certainty to evaluate the influence of individual link into the classification process. The link certainty is later combined with the content-only model to form a complex model named individual-link model. This new approach reduces the effects of loss of information. The author systematically evaluates new model on standard data sets to compare with traditional content-only methods and other link-based method to see the improvement of the new model. Besides the promising results when working on the graph of documents, the individual link model can be applied on the general graph of objects in the future.