No abstract

Keyword extraction approaches based on directed graph representation of text mostly use word positions in the sentences. A preceding word points to a succeeding word or vice versa in a window of N consecutive words in the text. The accuracy of this approach is dependent on the number of active voice and passive voice sentences in the given text. Edge direction can only be applied by considering the entire text as a single unit leaving no importance for the sentences in the document. Otherwise words at the initial or ending positions in each sentence will get less connections/recommendations. In this paper we propose a directed graph representation technique (Thematic text graph) in which weighted edges are drawn between the words based on the theme of the document. Keyword weights are identified from the Thematic text graph using an existing centrality measure and the resulting weights are used for computing the importance of sentences in the document. Experiments conducted on the benchmark data sets SemEval-2010 and DUC 2002 data sets shown that the proposed keyword weighting model is effective and facilitates an improvement in the quality of system generated extractive summaries.

The Personalized Privacy has drawn a lot of attention from diverse magnitudes of the public and various functional units like bureau of statistics, and hospitals. A large number of data publishing models and methods have been proposed and most of them focused on single sensitive attribute. A few research papers marked the need for preserving privacy of data consisting of multiple sensitive attributes. Applying the existing methods such as k-anonymity, l-diversity directly for publishing multiple sensitive attributes would minimize the utility of the data. Moreover, personalization has not been studied in this dimension. In this paper, we present a publishing model that manages personalization for publishing data with multiple sensitive attributes. The model uses slicing technique supported by deterministic anonymization for quasi identifiers; generalization for categorical sensitive attributes; and fuzzy approach for numerical sensitive attributes based on diversity. We cap the belief of an adversary inferring a sensitive value in a published data set to as high as that of an inference based on public knowledge. The experiments were carried out on census dataset and synthetic datasets. The results ensure that the privacy is being safeguarded without any compromise on the utility of the data.

Purpose Multimedia applications such as digital audio and video have stringent quality of service (QoS) requirement in mobile ad hoc network. To support wide range of QoS, complex routing protocols with multiple QoS constraints are necessary. In QoS routing, the basic problem is to find a path that satisfies multiple QoS constraints. Moreover, mobility, congestion and packet loss in dynamic topology of network also leads to QoS performance degradation of protocol. Design/methodology/approach In this paper, the authors proposed a multi-path selection scheme for QoS aware routing in mobile ad hoc network based on fractional cuckoo search algorithm (FCS-MQARP). Here, multiple QoS constraints energy, link life time, distance and delay are considered for path selection. Findings The experimentation of proposed FCS-MQARP is performed over existing QoS aware routing protocols AOMDV, MMQARP, CS-MQARP using measures such as normalized delay, energy and throughput. The extensive simulation study of the proposed FCS-based multipath selection shows that the proposed QoS aware routing protocol performs better than the existing routing protocol with maximal energy of 99.1501 and minimal delay of 0.0554. Originality/value This paper presents a hybrid optimization algorithm called the FCS algorithm for the multi-path selection. Also, a new fitness function is developed by considering the QoS constraints such as energy, link life time, distance and delay.

Abstract-In this paper we study the problem of protecting privacy in the publication of transactional data. Consider a collection of transactional data that contains detailed information about items bought together by individuals. Even after removing all personal characteristics of the buyer, which can serve as links to his identity, the publication of such data is still subject to privacy attacks from adversaries who have partial knowledge about the set. Unlike previous works, we do not distinguish data as sensitive and non-sensitive, but we consider them both as potential quasi-identifiers and potential sensitive data, depending on the point of view of the adversary. We define a new version of the anonymity guarantee using concept learning. Our anonymization model relies on generalization using concept hierarchy and concept learning. The proposed algorithms are experimentally evaluated using real world datasets.