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Prakash, M.
- Privacy Preservation of Micro Data Publishing using Fragmentation
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Authors
Affiliations
1 Department of Computer Science and Engineering, Anna University, Chennai, IN
2 Department of Computer Science and Engineering, SRM Institute of Science and Technology, IN
3 Department of Computing Science and Engineering, Galgotias University, IN
1 Department of Computer Science and Engineering, Anna University, Chennai, IN
2 Department of Computer Science and Engineering, SRM Institute of Science and Technology, IN
3 Department of Computing Science and Engineering, Galgotias University, IN
Source
ICTACT Journal on Soft Computing, Vol 9, No 3 (2019), Pagination: 1945-1949Abstract
Organization such as hospitals, publish detailed data or micro data about individuals for research or statistical purposes. Many applications that employ data mining techniques involve mining data that include private and sensitive information about the subjects. When releasing the micro data, it is necessary to prevent the sensitive information of the individuals from being disclosed. Several existing privacy-preserving approaches focus on anonymization techniques such as generalization and bucketization. Recent work has shown that generalization loses considerable amount of information for high dimensional data, the bucketization does not prevent membership disclosure and does not make clear separation between quasi-identifying attributes and sensitive attributes. In this work a novel technique called Fragmentation is proposed for publishing sensitive data with preventing the sensitive information of the individual. Here first the vertical Fragmentation is applied to attributes. In vertical Fragmentation, attributes are segmented into columns. Each column contains a subset of attributes. Secondly, the horizontal Fragmentation is applied to tuples. In this, tuples are segmented into buckets. Each bucket contains a subset of tuples. Finally the real dataset is used for experiments and the results show that this Fragmentation technique preserves better utility while protecting privacy threats and prevents the membership disclosure.Keywords
Privacy, Privacy Preservation, Data Anonymization, Data Publishing, Data Security.
References
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- Deepreply - An Automatic Email Reply System with Unsupervised Cloze Translation and Deep Learning
Abstract Views :193 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science and Engineering, Rajalakshmi Engineering College, IN
2 Department of Information Technology, Karpagam College of Engineering, IN
1 Department of Computer Science and Engineering, Rajalakshmi Engineering College, IN
2 Department of Information Technology, Karpagam College of Engineering, IN
Source
ICTACT Journal on Soft Computing, Vol 10, No 3 (2020), Pagination: 2090-2095Abstract
Electronic mail (E-mail) has been the primary mode of communication for official purposes and it continues to be the same in all work environments even today. With the growing number of emails and most of them requiring only trivial replies, more tools are needed to generate replies to emails by reusing past replies. Although there are expert systems that can assist us in replying to incoming emails, they produce a generic reply to all. So an intelligent system that can generate replies for an incoming email in a very precise manner and generating the text reply in the user’s style is the identified requirement. This work is divided into two portions. First, translating an incoming email into cloze representation and extract the entities from it for generating a context, question and answer triplets. This is used for synthesising the training data for Extractive Question Answering later. The mentioned triplets are generated from a corpus of random emails belonging to different contexts and then the answers are extracted by recognising the named entities and random phrases of nouns from these paragraphs. The second ploy is to find the similarity between an incoming email that requires a reply and an old email that contains the reply to it. As a solution to these challenges, we propose a new deep neural network-based approach that relies on coarse-grained sentence modelling using CNN and a LSTM model. Our experimental results show that the approach outperforms the state-of-the-art approaches that are existing on a cleaner corpus.Keywords
Deep Learning, E-mail, Unsupervised, Questioning.References
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