Multi-party computation (MPC) sorting and searching protocols are frequently used in different databases with varied applications, as in cooperative intrusion detection systems, private computation of set intersection and oblivious RAM. Ivan Damgard et al. have proposed two techniques i.e., bit-decomposition protocol and bit-wise less than protocol for MPC. These two protocols are used as building blocks and have proposed two oblivious MPC protocols. The proposed protocols are based on data-dependent algorithms such as insertion sort and binary search. The proposed multi-party sorting protocol takes the shares of the elements as input and outputs the shares of the elements in sorted order. The proposed protocol exhibits O ( 1 ) constant round complexity and O ( n log n ) communication complexity. The proposed multi-party binary search protocol takes two inputs. One is the shares of the elements in sorted order and the other one is the shares of the element to be searched. If the position of the search element exists, the protocol returns the corresponding shares, otherwise it returns shares of zero. The proposed multi-party binary search protocol exhibits O ( 1 ) round complexity and O ( n log n ) communication complexity. The proposed multi-party sorting protocol works better than the existing quicksort protocol when the input is in almost sorted order. The proposed multi-party searching protocol gives almost the same results, when compared to the general binary search algorithm.
With the increased use of social media many people misuse online platforms by uploading offensive content and sharing the same with vast audience. Here comes controlling of such offensive contents. In this work we concentrate on the issue of finding offensive text in social media. Existing offensive text detection systems treat weak pejoratives like ‘idiot‘ and extremely indecent pejoratives like ‘f***‘ as same as offensive irrespective of formal and informal contexts . In fact the weakly pejoratives in informal discussions among friends are casual and common which are not offensive but the same can be offensive when expressed in formal discussions. Crucial challenges to accomplish the task of role based offensive detection in text are i) considering the roles while classifying the text as offensive or not i) creating a contextual datasets including both formal and informal roles. To tackle the above mentioned challenges we develop deep neural network based model known as context aware role based offensive detection(CROD). We examine CROD on the manually created dataset that is collected from social networking sites. Results show that CROD gives better performance with RoBERTa with an accuracy of 94% while considering the context and role in data specifics.
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