A Model to Publish Online Social Networks Data with Privacy Preserving
| Autor: | Rohulla Kosari Langari, Soheila Sardar, Seyed Abdollah Amin Mousavi, Reza Radfar |
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| Jazyk: | perština |
| Rok vydání: | 2019 |
| Předmět: | |
| Zdroj: | مطالعات مدیریت کسب و کار هوشمند, Vol 8, Iss 29, Pp 87-112 (2019) |
| Druh dokumentu: | article |
| ISSN: | 2821-0964 2821-0816 |
| DOI: | 10.22054/ims.2019.10377 |
| Popis: | Nowadays the growth in the use of social networks among different classes of world community is increasingly undeniable. Social networks database include Rich and valuable resources whose release and analysis with the purpose of marketing, publicity, National Security, Health and etc. can benefit researchers of public and private institutions. But respect the privacy of the entities whose information is available to data miner analysis is essential as a legal protocol. In this Paper, through qualitative methodology Meta synthesis, all related dimensions, indicators and codes were identified and then the importance and priority of each of the factors was determined. Subsequently, the improved model of anonymity was presented by an optimizing firefly algorithm and fuzzy clustering. The result of simulation and assessment of the proposed model on the data of four social networks such as Facebook, YouTube, Twitter and Google+ depicts that privacy preserving of data with the lowest distortion ratio and the more usefulness of data. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
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