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Call for Contributions
| Content Provider | Semantic Scholar |
|---|---|
| Author | Agrawal, Vishwani D. Saluja, Kewal K. Singh, Adit D. Biswas, Gautam |
| Copyright Year | 2016 |
| Abstract | The recent data science development such as molecular modeling, genetic sequencing, health record processing, and smart city projects demonstrated the future trends on Big data applications. Big data is a collection of huge volume of structured and unstructured data that are so large and difficult to get process using traditional databases and software technologies. Cloud computing is the most suitable solution for the management of such kind of data. In fact, it provides strong storage, computation and distributed capability in support of Big Data processing. Due to the explosion of web-based services, unstructured data management and social media and mobile computing, the amount of data to be handled has increased from terabytes to petabytes and zetabytes in just two decades. By 2020, we are expecting a growth of 2.314 zetabytes (2 214 exabytes, where 1 exabytes = 1 billion gigabytes). Storage, management and analysis of large quantities of such data could raise significant privacy challenges. Substantially, Big Data may result from combining data from numerous sources, which may reveal the privacy of the individuals (aggregation problem). Another security challenge of Big Data is access control. Hence, the question is how can access control policies for different types of data such as structured, semi-structured, unstructured and graph data be integrated? Furthermore, Big Data management and analytics raise another challenge, which is securing the infrastructures. In fact, most of the technologies that were proposed for the Big Data management such as Haddop, MapReduce, Hive, Cassandr, and Strom – present several security challenges. Finally, due to the Cloud distribution feature, the conventional data acquisition regulation is not sufficient to meet the digital forensics evidence requirements. Substantially, it is almost impossible to seize a physical hard drive to get all the related forensics evidences. Further, it is very difficult to identify which physical hard is compromised. |
| File Format | PDF HTM / HTML |
| DOI | 10.1215/08992363-21-1-vi |
| Alternate Webpage(s) | https://icpe2016.spec.org/uploads/media/ICPE2016_Call_for_Contributions.pdf |
| Alternate Webpage(s) | https://www.uni-kassel.de/fb05/uploads/media/CfC_DE__01.pdf |
| Alternate Webpage(s) | https://www.sefi.be/wp-content/uploads/2019/12/Call-for-papers-SEFI2020.pdf |
| Alternate Webpage(s) | https://www.iaria.org/conferences2017/filesCOGNITIVE17/COGWEB.pdf |
| Alternate Webpage(s) | https://www.iaria.org/conferences2019/filesICWMC19/BigSecCloud.pdf |
| Alternate Webpage(s) | https://doi.org/10.1215/08992363-21-1-vi |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
