dc.contributor.advisor | Hossain, Muhammad Iqbal | |
dc.contributor.author | Khilji, Ishfaque Qamar | |
dc.contributor.author | Saha, Kamonashish | |
dc.contributor.author | Shonon, Jushan Amin | |
dc.contributor.author | Israq, Ragib | |
dc.date.accessioned | 2021-09-29T07:32:22Z | |
dc.date.available | 2021-09-29T07:32:22Z | |
dc.date.copyright | 2019 | |
dc.date.issued | 2019-12 | |
dc.identifier.other | ID: 15301113 | |
dc.identifier.other | ID: 15341004 | |
dc.identifier.other | ID: 19241042 | |
dc.identifier.other | ID: 19341032 | |
dc.identifier.uri | http://hdl.handle.net/10361/15076 | |
dc.description | Cataloged from PDF version of thesis. | |
dc.description | Includes bibliographical references (pages 41-43). | |
dc.description | This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2019. | en_US |
dc.description.abstract | In today’s world, machine learning has become a big factor. It not only needs to
be helpful, but also accurate and precise prediction is required. Machine learning is
now becoming a widely used mechanism and applying it in certain sensitive fields
like medical and financial data has only made things easier, but it also brought
some difficulty in data privacy and data security which will protect the complete
implementation of cloud based machine learning for these aspects due to the law
and ethical needs. In this project, to give proper solution, we have come up with
the idea using concepts of CryptoNets and Neural Networks, where we will be able
to convert the learned neural network with the encrypted data to Cryptonets and
the data will be totally encrypted and this will prevent the chances of unencrypted
data being available to everyone. In this method, the owner will send the encrypted
data to the cloud first and will hold a private key which can be used to decrypt
the data later on. The cloud will have no idea about the data there since it will
be in encrypted form and any attempts to get data from the cloud will only give
the encrypted form. However, applying neural network to the cloud will enable us
to store the data and make predictions in encrypted form and also give back the
encrypted data to the user. In this way, the cloud will have no idea about the actual
data and after the prediction is made, it will give back the predicted data in the
encrypted form. We were able to achieve an encrypted prediction of about 78% close
to the validation accuracy amount we achieved when training our Neural Network
model. | en_US |
dc.description.statementofresponsibility | Ishfaque Qamar Khilji | |
dc.description.statementofresponsibility | Kamonashish Saha | |
dc.description.statementofresponsibility | Jushan Amin Shonon | |
dc.description.statementofresponsibility | Ragib Israq | |
dc.format.extent | 43 Pages | |
dc.language.iso | en_US | en_US |
dc.publisher | BRAC University | en_US |
dc.rights | Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. | |
dc.subject | Crypto-Nets | en_US |
dc.subject | Neural Network | en_US |
dc.subject | Machine learning | en_US |
dc.subject | Neural Network model | en_US |
dc.title | Prediction of acute lymphoid leukemia using Privacy Preserving Neural Network | en_US |
dc.type | Thesis | en_US |
dc.contributor.department | Department of Computer Science and Engineering, Brac University | |
dc.description.degree | B. Computer Science | |