Matches in SemOpenAlex for { <https://semopenalex.org/work/W2943566172> ?p ?o ?g. }
Showing items 1 to 83 of
83
with 100 items per page.
- W2943566172 endingPage "22" @default.
- W2943566172 startingPage "13" @default.
- W2943566172 abstract "Cloud computing provides capable ascendable IT edifice to provision numerous processing of a various big data applications in sectors such as healthcare and business. Mainly electronic health records data sets and in such applications generally contain privacy-sensitive data. The most popular technique for data privacy preservation is anonymizing the data through generalization. Proposal is to examine the issue against proximity privacy breaches for big data anonymization and try to recognize a scalable solution to this issue. Scalable clustering approach with two phase consisting of clustering algorithm and K-Anonymity scheme with Generalisation and suppression is intended to work on this problem. Design of the algorithms is done with MapReduce to increase high scalability by carrying out dataparallel execution in cloud. Wide-ranging researches on actual data sets substantiate that the method deliberately advances the competence of defensive proximity privacy breaks, the scalability and the efficiency of anonymization over existing methods. Anonymizing data sets through generalization to gratify some of the privacy attributes like k- Anonymity is a popularly-used type of privacy preserving methods. Currently, the gauge of data in numerous cloud surges extremely in agreement with the Big Data, making it a dare for frequently used tools to actually get, manage, and process large-scale data for a particular accepted time scale. Hence, it is a trial for prevailing anonymization approaches to attain privacy conservation for big data private information due to scalabilty issues." @default.
- W2943566172 created "2019-05-09" @default.
- W2943566172 creator A5004047037 @default.
- W2943566172 creator A5033142067 @default.
- W2943566172 date "2018-06-30" @default.
- W2943566172 modified "2023-09-26" @default.
- W2943566172 title "Data Anonymization for Privacy Preservation in Big Data" @default.
- W2943566172 hasPublicationYear "2018" @default.
- W2943566172 type Work @default.
- W2943566172 sameAs 2943566172 @default.
- W2943566172 citedByCount "0" @default.
- W2943566172 crossrefType "journal-article" @default.
- W2943566172 hasAuthorship W2943566172A5004047037 @default.
- W2943566172 hasAuthorship W2943566172A5033142067 @default.
- W2943566172 hasConcept C111919701 @default.
- W2943566172 hasConcept C119857082 @default.
- W2943566172 hasConcept C123201435 @default.
- W2943566172 hasConcept C124101348 @default.
- W2943566172 hasConcept C134306372 @default.
- W2943566172 hasConcept C177148314 @default.
- W2943566172 hasConcept C178005623 @default.
- W2943566172 hasConcept C2522767166 @default.
- W2943566172 hasConcept C2776945810 @default.
- W2943566172 hasConcept C2777706471 @default.
- W2943566172 hasConcept C33923547 @default.
- W2943566172 hasConcept C38652104 @default.
- W2943566172 hasConcept C41008148 @default.
- W2943566172 hasConcept C48044578 @default.
- W2943566172 hasConcept C73555534 @default.
- W2943566172 hasConcept C75684735 @default.
- W2943566172 hasConcept C77088390 @default.
- W2943566172 hasConcept C79974875 @default.
- W2943566172 hasConcept C99221444 @default.
- W2943566172 hasConceptScore W2943566172C111919701 @default.
- W2943566172 hasConceptScore W2943566172C119857082 @default.
- W2943566172 hasConceptScore W2943566172C123201435 @default.
- W2943566172 hasConceptScore W2943566172C124101348 @default.
- W2943566172 hasConceptScore W2943566172C134306372 @default.
- W2943566172 hasConceptScore W2943566172C177148314 @default.
- W2943566172 hasConceptScore W2943566172C178005623 @default.
- W2943566172 hasConceptScore W2943566172C2522767166 @default.
- W2943566172 hasConceptScore W2943566172C2776945810 @default.
- W2943566172 hasConceptScore W2943566172C2777706471 @default.
- W2943566172 hasConceptScore W2943566172C33923547 @default.
- W2943566172 hasConceptScore W2943566172C38652104 @default.
- W2943566172 hasConceptScore W2943566172C41008148 @default.
- W2943566172 hasConceptScore W2943566172C48044578 @default.
- W2943566172 hasConceptScore W2943566172C73555534 @default.
- W2943566172 hasConceptScore W2943566172C75684735 @default.
- W2943566172 hasConceptScore W2943566172C77088390 @default.
- W2943566172 hasConceptScore W2943566172C79974875 @default.
- W2943566172 hasConceptScore W2943566172C99221444 @default.
- W2943566172 hasIssue "6" @default.
- W2943566172 hasLocation W29435661721 @default.
- W2943566172 hasOpenAccess W2943566172 @default.
- W2943566172 hasPrimaryLocation W29435661721 @default.
- W2943566172 hasRelatedWork W1998654583 @default.
- W2943566172 hasRelatedWork W2005558540 @default.
- W2943566172 hasRelatedWork W2005991660 @default.
- W2943566172 hasRelatedWork W2040682932 @default.
- W2943566172 hasRelatedWork W2155598271 @default.
- W2943566172 hasRelatedWork W2182794635 @default.
- W2943566172 hasRelatedWork W2512900577 @default.
- W2943566172 hasRelatedWork W2547823193 @default.
- W2943566172 hasRelatedWork W2549955619 @default.
- W2943566172 hasRelatedWork W2732338097 @default.
- W2943566172 hasRelatedWork W2747066588 @default.
- W2943566172 hasRelatedWork W2766461310 @default.
- W2943566172 hasRelatedWork W2890405359 @default.
- W2943566172 hasRelatedWork W2898931495 @default.
- W2943566172 hasRelatedWork W2967268087 @default.
- W2943566172 hasRelatedWork W2990267638 @default.
- W2943566172 hasRelatedWork W3021771079 @default.
- W2943566172 hasRelatedWork W3185327010 @default.
- W2943566172 hasRelatedWork W2599252980 @default.
- W2943566172 hasRelatedWork W2992782483 @default.
- W2943566172 hasVolume "6" @default.
- W2943566172 isParatext "false" @default.
- W2943566172 isRetracted "false" @default.
- W2943566172 magId "2943566172" @default.
- W2943566172 workType "article" @default.