The Third IEEE International Conference on Big Data Security on Cloud, IEEE BigDataSecurity 2017 : The Third IEEE International Conference on High Performance and Smart Computing, IEEE HPSC 2017 ; the Second IEEE International Conferenc...最新文献
Pub Date : 2019-10-21DOI: 10.1515/9783110606058-003
Souvik Chowdhury, Shibakali Gupta
{"title":"3. Anomaly detection in cloud big database metric","authors":"Souvik Chowdhury, Shibakali Gupta","doi":"10.1515/9783110606058-003","DOIUrl":"https://doi.org/10.1515/9783110606058-003","url":null,"abstract":"","PeriodicalId":93151,"journal":{"name":"The Third IEEE International Conference on Big Data Security on Cloud, IEEE BigDataSecurity 2017 : The Third IEEE International Conference on High Performance and Smart Computing, IEEE HPSC 2017 ; the Second IEEE International Conferenc...","volume":"06 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78097093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-21DOI: 10.1515/9783110606058-006
S. Koley
: Big data is a collection of huge sets of data with different categories where it could be distinguished as structured and unstructured data. As we are revolutioniz-ing to zeta bytes from Giga/tera/peta/exabytes in this phase of computing, the threats have also increased in parallel. Besides big organizations, cost reduction is the criterion for the use of small- and medium-sized organizations too, thereby increasing the security threat. Checking of the streaming data once is not the solution as security breaches cannot be understood. The data stack up within the clouds is not the only preference as big data technology is available for dispensation of both structured and unstructured data. Nowadays, an enormous quantity of data is provoked by mobile phones (smart-phone) or equally the symphony form. Big data architecture is comprehended among the mobile cloud designed for supreme consumption. The best ever implementation is able to be conked out realistically to make use of a novel data-centric architecture of MapReduce technology, while Hadoop distributed file system also acts with immense liability in using data with divergent arrangement. As time approaches, the level of information and data engendered from different sources enhanced and faster execution is the claim for the same. In this chapter our aim is to find out big data security that is vulnerable and also to find out the best possible solutions for them. We consider that this attempt will dislodge a stride for-ward along the way to an improved evolution in secure propinquity to opportunity.
{"title":"6. Big data security issues with challenges and solutions","authors":"S. Koley","doi":"10.1515/9783110606058-006","DOIUrl":"https://doi.org/10.1515/9783110606058-006","url":null,"abstract":": Big data is a collection of huge sets of data with different categories where it could be distinguished as structured and unstructured data. As we are revolutioniz-ing to zeta bytes from Giga/tera/peta/exabytes in this phase of computing, the threats have also increased in parallel. Besides big organizations, cost reduction is the criterion for the use of small- and medium-sized organizations too, thereby increasing the security threat. Checking of the streaming data once is not the solution as security breaches cannot be understood. The data stack up within the clouds is not the only preference as big data technology is available for dispensation of both structured and unstructured data. Nowadays, an enormous quantity of data is provoked by mobile phones (smart-phone) or equally the symphony form. Big data architecture is comprehended among the mobile cloud designed for supreme consumption. The best ever implementation is able to be conked out realistically to make use of a novel data-centric architecture of MapReduce technology, while Hadoop distributed file system also acts with immense liability in using data with divergent arrangement. As time approaches, the level of information and data engendered from different sources enhanced and faster execution is the claim for the same. In this chapter our aim is to find out big data security that is vulnerable and also to find out the best possible solutions for them. We consider that this attempt will dislodge a stride for-ward along the way to an improved evolution in secure propinquity to opportunity.","PeriodicalId":93151,"journal":{"name":"The Third IEEE International Conference on Big Data Security on Cloud, IEEE BigDataSecurity 2017 : The Third IEEE International Conference on High Performance and Smart Computing, IEEE HPSC 2017 ; the Second IEEE International Conferenc...","volume":"133 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79664686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-21DOI: 10.1515/9783110606058-001
Shibakali Gupta, Dr. Indradip Banerjee, S. Bhattacharyya
{"title":"1. Introduction","authors":"Shibakali Gupta, Dr. Indradip Banerjee, S. Bhattacharyya","doi":"10.1515/9783110606058-001","DOIUrl":"https://doi.org/10.1515/9783110606058-001","url":null,"abstract":"","PeriodicalId":93151,"journal":{"name":"The Third IEEE International Conference on Big Data Security on Cloud, IEEE BigDataSecurity 2017 : The Third IEEE International Conference on High Performance and Smart Computing, IEEE HPSC 2017 ; the Second IEEE International Conferenc...","volume":"91 3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80642986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-21DOI: 10.1515/9783110606058-004
Shibakali Gupta, A. Mukherjee
: Nowadays, in the fast-paced world of Google and Facebook, every detail of human being could be considered as a set of data or array of data that can be stored, verified, and processed in several ways for the benefits of users. Big data would be perfectly described with humongous large and complex data entities, where classic approach application software is incompetent for them. Big data epitomizes the evidence chattels classified by a high volume, velocity, and variability to require precise technology and analytical approaches for its transformation into value. Big data include netting data, search, data stowing, transmission, updating, data scrutiny, visualization, sharing, querying, data source, and information confidentiality. Big data can castoff in innumerable sectors like defense, health care, and Internet of things. The most famous example probably being Palantir, which was primarily sponsored by the Intelligence Its primary was to deliver analytics sway in the war against terrorism of any kind but with accumulative dependency on big data, the menace of exploitation of this data also arises. The prominence of big data does not gyrate around data magnitude or dimensions rather it revolves around how you process it. You can consider stats from whichever cradle and analyze it to discover answers that facilitate cost diminutions, interval time declines, fresh product development and elevated offerings, and smart management. When you conglomer-ate big data with efficient and dynamic analytics, you can achieve business-corre-lated tasks such as detecting fraudulent behavior, recalculating entire risk portfolios in shorter span of time, determining root causes of failures, disputes, and blemishes in near real time. Few instances such as Cambridge Analytica lighten the insight of the exploitation of the big data. There are several instances where large amount of data has been stolen like in 2014, Yahoo Inc., where 3 billion accounts were effec-tively according to official sources in 2016, Adult Friend Finder where 412.2 million accounts were effected with credit card details of an event that is not a requisite illegal, but sketchy to say the least. The statistic that several sets of international figures were acknowledged in this bulk data set is what marks the news. With the evolution of big data, it makes treasured visions for hackers invariably tempting, but it also provides a big structure of data that con-verts it to payload utmost necessary to protect. In such a scenario, the security of big data is very important. This chapter shares sheer insight of how big data can be used in hacking and social engineering. This chapter will try to list down the ways big data is mined from various sources such as Google Services of Android and Facebook. It will list the various ways the big data is used in day-to-day life by the given companies and other advertising companies. This chapter will try to enlist all the major ill ways this data can be use
{"title":"4. Use of big data in hacking and social engineering","authors":"Shibakali Gupta, A. Mukherjee","doi":"10.1515/9783110606058-004","DOIUrl":"https://doi.org/10.1515/9783110606058-004","url":null,"abstract":": Nowadays, in the fast-paced world of Google and Facebook, every detail of human being could be considered as a set of data or array of data that can be stored, verified, and processed in several ways for the benefits of users. Big data would be perfectly described with humongous large and complex data entities, where classic approach application software is incompetent for them. Big data epitomizes the evidence chattels classified by a high volume, velocity, and variability to require precise technology and analytical approaches for its transformation into value. Big data include netting data, search, data stowing, transmission, updating, data scrutiny, visualization, sharing, querying, data source, and information confidentiality. Big data can castoff in innumerable sectors like defense, health care, and Internet of things. The most famous example probably being Palantir, which was primarily sponsored by the Intelligence Its primary was to deliver analytics sway in the war against terrorism of any kind but with accumulative dependency on big data, the menace of exploitation of this data also arises. The prominence of big data does not gyrate around data magnitude or dimensions rather it revolves around how you process it. You can consider stats from whichever cradle and analyze it to discover answers that facilitate cost diminutions, interval time declines, fresh product development and elevated offerings, and smart management. When you conglomer-ate big data with efficient and dynamic analytics, you can achieve business-corre-lated tasks such as detecting fraudulent behavior, recalculating entire risk portfolios in shorter span of time, determining root causes of failures, disputes, and blemishes in near real time. Few instances such as Cambridge Analytica lighten the insight of the exploitation of the big data. There are several instances where large amount of data has been stolen like in 2014, Yahoo Inc., where 3 billion accounts were effec-tively according to official sources in 2016, Adult Friend Finder where 412.2 million accounts were effected with credit card details of an event that is not a requisite illegal, but sketchy to say the least. The statistic that several sets of international figures were acknowledged in this bulk data set is what marks the news. With the evolution of big data, it makes treasured visions for hackers invariably tempting, but it also provides a big structure of data that con-verts it to payload utmost necessary to protect. In such a scenario, the security of big data is very important. This chapter shares sheer insight of how big data can be used in hacking and social engineering. This chapter will try to list down the ways big data is mined from various sources such as Google Services of Android and Facebook. It will list the various ways the big data is used in day-to-day life by the given companies and other advertising companies. This chapter will try to enlist all the major ill ways this data can be use","PeriodicalId":93151,"journal":{"name":"The Third IEEE International Conference on Big Data Security on Cloud, IEEE BigDataSecurity 2017 : The Third IEEE International Conference on High Performance and Smart Computing, IEEE HPSC 2017 ; the Second IEEE International Conferenc...","volume":"63 5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87742869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-21DOI: 10.1515/9783110606058-fm
{"title":"Frontmatter","authors":"","doi":"10.1515/9783110606058-fm","DOIUrl":"https://doi.org/10.1515/9783110606058-fm","url":null,"abstract":"","PeriodicalId":93151,"journal":{"name":"The Third IEEE International Conference on Big Data Security on Cloud, IEEE BigDataSecurity 2017 : The Third IEEE International Conference on High Performance and Smart Computing, IEEE HPSC 2017 ; the Second IEEE International Conferenc...","volume":"154 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77706858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-21DOI: 10.1515/9783110606058-005
Srilekha Mukherjee, G. Sanyal
{"title":"5. Steganography, the widely used name for data hiding","authors":"Srilekha Mukherjee, G. Sanyal","doi":"10.1515/9783110606058-005","DOIUrl":"https://doi.org/10.1515/9783110606058-005","url":null,"abstract":"","PeriodicalId":93151,"journal":{"name":"The Third IEEE International Conference on Big Data Security on Cloud, IEEE BigDataSecurity 2017 : The Third IEEE International Conference on High Performance and Smart Computing, IEEE HPSC 2017 ; the Second IEEE International Conferenc...","volume":"115 15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84185549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-01-01DOI: 10.1109/bigdatasecurity.2017.10
Samuel S Wu, Shigang Chen, Abhishek Bhattacharjee, Ying He
An integral part of any social or medical research is the availability of reliable data. For the integrity of participants' responses, a secure environment for collecting sensitive data is required. This paper introduces a novel privacy-preserving data collection method: collusion resistant multi-matrix masking (CRM3). The CRM3 method requires multiple masking service providers (MSP), each generating its own random masking matrices. The key step is that each participant's data is randomly decomposed into the sum of component vectors, and each component vector is sent to the MSPs for masking in a different order. The CRM3 method publicly releases two sets of masked data: one being right multiplied by random invertible matrices and the other being left multiplied by random orthogonal matrices. Both MSPs and the released data may be hosted on cloud platforms. Our data collection and release procedure is designed so that MSPs and the data collector are not able to derive the original participants' data hence providing strong privacy protection. However, statistical inference on parameters of interest will produce exactly the same results from the masked data as from the original data, under commonly used statistical methods such as general linear model, contingency table analysis, logistic regression, and Cox proportional hazard regression.
{"title":"Collusion Resistant Multi-Matrix Masking for Privacy-Preserving Data Collection.","authors":"Samuel S Wu, Shigang Chen, Abhishek Bhattacharjee, Ying He","doi":"10.1109/bigdatasecurity.2017.10","DOIUrl":"https://doi.org/10.1109/bigdatasecurity.2017.10","url":null,"abstract":"<p><p>An integral part of any social or medical research is the availability of reliable data. For the integrity of participants' responses, a secure environment for collecting sensitive data is required. This paper introduces a novel privacy-preserving data collection method: <i>collusion resistant multi-matrix masking</i> (CRM<sup>3</sup>). The CRM<sup>3</sup> method requires multiple masking service providers (MSP), each generating its own random masking matrices. The key step is that each participant's data is randomly decomposed into the sum of component vectors, and each component vector is sent to the MSPs for masking in a different order. The CRM<sup>3</sup> method publicly releases two sets of masked data: one being right multiplied by random invertible matrices and the other being left multiplied by random orthogonal matrices. Both MSPs and the released data may be hosted on cloud platforms. Our data collection and release procedure is designed so that MSPs and the data collector are not able to derive the original participants' data hence providing strong privacy protection. However, statistical inference on parameters of interest will produce exactly the same results from the masked data as from the original data, under commonly used statistical methods such as general linear model, contingency table analysis, logistic regression, and Cox proportional hazard regression.</p>","PeriodicalId":93151,"journal":{"name":"The Third IEEE International Conference on Big Data Security on Cloud, IEEE BigDataSecurity 2017 : The Third IEEE International Conference on High Performance and Smart Computing, IEEE HPSC 2017 ; the Second IEEE International Conferenc...","volume":"2017 ","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/bigdatasecurity.2017.10","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38555119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1993-11-01DOI: 10.1515/9783110612400-007
Shibakali Gupta, Dr. Indradip Banerjee, S. Bhattacharyya
{"title":"7. Conclusions","authors":"Shibakali Gupta, Dr. Indradip Banerjee, S. Bhattacharyya","doi":"10.1515/9783110612400-007","DOIUrl":"https://doi.org/10.1515/9783110612400-007","url":null,"abstract":"","PeriodicalId":93151,"journal":{"name":"The Third IEEE International Conference on Big Data Security on Cloud, IEEE BigDataSecurity 2017 : The Third IEEE International Conference on High Performance and Smart Computing, IEEE HPSC 2017 ; the Second IEEE International Conferenc...","volume":"44 12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"1993-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72991129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Third IEEE International Conference on Big Data Security on Cloud, IEEE BigDataSecurity 2017 : The Third IEEE International Conference on High Performance and Smart Computing, IEEE HPSC 2017 ; the Second IEEE International Conferenc...
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