Multisource keyword extraction and graph construction for privacy preservation

Privacy preservation is an important branch of Data Mining which handles hiding of an individual's sensitive data without affecting the data usability. This paper proposes a new technique to provide privacy preservation of sensitive data based on the semantic context. Multisource Keyword Extraction and Graph Construction for Privacy Preservation involves extracting keywords from various data formats and preserving privacy among the keywords extracted using the techniques of Vector Marking. Initially, data cleaning and preprocessing is done on the document to extract keywords by applying techniques such as parsing, duplicate elimination, stemming and indexing. The document can be either PDF, SQL or Word files. After preprocessing, a context graph is generated from the keywords extracted with the help of context dictionaries such as WordNet and DBpedia. This context graph acts as a primary source of reference for all user queries. Privacy preservation of sensitive information is achieved using various Vector Marking techniques. The data input by the user can be classified as structured, unstructured and semi-structured data. Appropriate Vector Marking approaches are used for the given input data format. The keyword specified by the user in the input data as private is queried in the context graph to obtain the correlated words and these words are hidden from the access of the other users. Thus solving some of the issues related to privacy leakage. © 2017 ACM

SWCTE: Semantic weighted context tagging engine for privacy preserving data mining

Privacy Preserving in Data Mining is a very important area which deals with hiding an individual's sensitive data without affecting the usability of data. In this paper, we put forward a technique to provide privacy preservation of sensitive data based on the semantic context. Our approach encapsulates various techniques of Text-processing, keyphrase extraction, Cooccurrence analysis, ontology construction and query analysis. To handle privacy issues Correlation Based Transformation Strategy (CBTS) is performed on sensitive data, additionally we can add custom properties to the attributes of the ontology to indicate the sensitive data. Our experimental results indicate that our solution is effective in marking the private data using the semantic context of the input text. The main goal of our work is to construct a module which acts as an intermediate step in pre-processing for data mining while preserving the privacy. © 2016 IEEE