Papers by Brijesh B Mehta
Big data is collected and processed using different sources and tools that lead to privacy issues... more Big data is collected and processed using different sources and tools that lead to privacy issues. Privacy preserving data publishing techniques such as k-anonymity, l-diversity, and t-closeness are used to de-identify the data; however, the chances of re-identification are always remain present since data is collected from multiple sources. Owing to the large volume of data, less generalisation or suppression is required to achieve the same level of privacy, which is also known as ‘large crowd effect’, although it is always challenging to handle such a large data for anonymization. MapReduce handles large volume of data and distributes the data into the smaller chunks across the multiple nodes; consequently, the full advantage of large volume of data is underachieved. Therefore, scalability of privacy preserving techniques becomes a challenging area of research. The authors explore this area and propose an algorithm named scalable k-anonymization (SKA) using MapReduce for privacy preserving big data publishing. The authors also compare the approach with existing approaches that results into a remarkable improvement of the data utility and significantly enhances the performance in terms of running time.
DESCRIPTION This is the poster prepared for Security and Privacy Symposium 2015 at IIIT, Delhi.
Big data analytics has created opportunities for researchers to process huge amount of data but c... more Big data analytics has created opportunities for researchers to process huge amount of data but created a big threat to privacy of individual. Data processed by big data analytics platforms may have personal information which need to be taken care of when deriving some useful results for research. Existing privacy preserving techniques like, anonymization requires having dataset divided in the set of attributes like, sensitive attributes, quasi identifiers, and non-sensitive attributes. With the structured data it may possible to have such a distribution but in unstructured data it is very difficult to identify sensitive attribute and quasi identifiers.
Big data is large and complex data that is difficult to process by traditional data processing sy... more Big data is large and complex data that is difficult to process by traditional data processing systems. Big data an-alytics is a process to generate knowledge from large datasets, having variety of data, which is collected from multiple sources, using platforms such as, high performance computing clusters, hadoop, spark, etc. Due to data collection from multiple sources, chances of privacy breach have increased. It is difficult to apply existing privacy models (privacy preserving techniques) in big data analytics because of 3Vs: Volume (large amount of data), Variety (structured, semistructured or un-structured data), and Velocity (fast generation and processing of data), characteristics of big data. This paper discusses about general architecture of big data analytics that shows different stages of big data analytics, which can be helpful to identify the stage, where privacy models can be applied. Based on survey of existing privacy models, a summery has been prepared, that shows relation between privacy models and 3Vs of big data.
2014 Conference on IT in Business, Industry and Government (CSIBIG), Mar 2014
Digital multimedia watermarking technology was suggested in the last decade to embed copyright in... more Digital multimedia watermarking technology was suggested in the last decade to embed copyright information in digital objects such as images, audio and video. However, the increasing use of relational database systems in many real-life applications created the need for database watermarking systems for protection of database. As a result, watermarking relational database systems is now merging as a research area that deals with the legal issue of copyright protection of database systems.Therefore, an evolution of watermarking has been started with Relational database. It all started with the first method proposed in 2002 by agrawal and kiernan for watermarking in relational database. Then there are so many methods have been proposed and implemented by many researchers. We are going to see the evolution of database watermarking for security in database in this paper.
International Journal of Computer Trends and Technology (IJCTT) – volume 7 number 3, Jan 2014
Spatial data mining or Knowledge discovery in spatial database is the extraction of implicit know... more Spatial data mining or Knowledge discovery in spatial database is the extraction of implicit knowledge, spatial relations and spatial
patterns that are not explicitly stored in databases. Co-location patterns discovery is the process of finding the subsets of features that are frequently located together in the same geographic area. In this paper, we discuss the different approaches like Rule based approach, Join-less approach, Partial Join approach and Constraint neighborhood based approach for finding co-location patterns.
International Journal of Computer Technology & Applications, Vol 4 (1), pp. 84-86, ISSN:2229-6093, Feb 2013
""Face recognition presents a challenging problem in the field of image analysis and computer vis... more ""Face recognition presents a challenging problem in the field of image analysis and computer vision. The security of information is becoming very significant and difficult. Security cameras are presently common in airports, Offices, University, ATM, Bank and in any locations with a security system. Face recognition is a biometric system used to identify or verify a person from a digital image. Face Recognition system is used in security. Face recognition system should be able to automatically detect a face in an image. This involves extracts its features and then recognize it, regardless of lighting, expression, illumination, ageing, transformations (translate, rotate and scale image) and
pose, which is a difficult task. This paper contains three sections.
The first section describes the common methods like holistic matching method, feature extraction method and hybrid methods. The second section describes applications with examples and finally third section describes the future research directions of face recognition.""
International Conference on Security and Management (SAM), Jul 2011
Digital multimedia watermarking technology had suggested in the last decade to embed copyright in... more Digital multimedia watermarking technology had suggested in the last decade to embed copyright information in digital objects such as images, audio and video. However, the increasing use of relational database systems in many real-life applications created an ever-increasing need for watermarking database systems. As a result, watermarking relational database systems is now merging as a research area that deals with the legal issue of copyright protection of database systems. The main goal of database watermarking is to generate robust and impersistant watermark for database. In this paper we propose a method, based on image as watermark and this watermark is embedded over the database at two different attribute of tuple, one in the numeric attribute of tuple and another in the date attribute’s time (
seconds) field. Our approach can be applied for numerical and
categorical database.
Thesis Chapters by Brijesh B Mehta
As we know, watermarking is firstly, introduced in image processing and then it extended to secur... more As we know, watermarking is firstly, introduced in image processing and then it extended to security of text and multimedia data. Now days, it also used for database and software. There is so much work done so far by many researches in the watermarking multimedia data [1] [2] [3]. Most of this method were initially developed for images [4] and later extended to video [5] and audio data [6][7]. Software watermarking techniques [8][9][10] , also been introduced but it did not get much success. Because of, differences between multimedia and database we cannot directly use any of the technique as it is for database, which developed for multimedia data. These differences include [13]:
Books by Brijesh B Mehta
Lambert Academic Publishing, 2013
A face recognition system that solves the problem of changes in facial expression and mimics in 3... more A face recognition system that solves the problem of changes in facial expression and mimics in 3D range images. So here, we propose a local variation detection and restoration method based eigenfaces using the principal component analysis (PCA). The depth map of a 3D facial image is first smoothed using median filter to minimize the local variation. The forefront nose point is selected to be the image center for alignment. The detected face shape is cropped & normalized to a standard image size of 101x101 pixels. Facial depth-valus are scaled between 0 and 255 for translation and scaling-invariant identification. The preprocessed face image is smoothed to minimize the local variations. The PCA is applied to the resultant range data and the corresponding principal or Eigen images are used as the characteristic feature vectors of the subject to find the person identity in the database of pre-recorded faces. The system performance is tested on the GavabDB databases. Experimental results show that the proposed method is able to identify subjects with different facial expression and mimics in the presence of noise in their 3D facial images.
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Papers by Brijesh B Mehta
patterns that are not explicitly stored in databases. Co-location patterns discovery is the process of finding the subsets of features that are frequently located together in the same geographic area. In this paper, we discuss the different approaches like Rule based approach, Join-less approach, Partial Join approach and Constraint neighborhood based approach for finding co-location patterns.
pose, which is a difficult task. This paper contains three sections.
The first section describes the common methods like holistic matching method, feature extraction method and hybrid methods. The second section describes applications with examples and finally third section describes the future research directions of face recognition.""
seconds) field. Our approach can be applied for numerical and
categorical database.
Thesis Chapters by Brijesh B Mehta
Books by Brijesh B Mehta
patterns that are not explicitly stored in databases. Co-location patterns discovery is the process of finding the subsets of features that are frequently located together in the same geographic area. In this paper, we discuss the different approaches like Rule based approach, Join-less approach, Partial Join approach and Constraint neighborhood based approach for finding co-location patterns.
pose, which is a difficult task. This paper contains three sections.
The first section describes the common methods like holistic matching method, feature extraction method and hybrid methods. The second section describes applications with examples and finally third section describes the future research directions of face recognition.""
seconds) field. Our approach can be applied for numerical and
categorical database.