TY - GEN
T1 - Privacy Preserving Big Data Publication on Cloud Using Mondrian Anonymization Techniques and Deep Neural Networks
AU - Andrew, J.
AU - Karthikeyan, J.
AU - Jebastin, Jeffy
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/3
Y1 - 2019/3
N2 - In recent trends, privacy preservation is the most predominant factor, on big data analytics and cloud computing. Every organization collects personal data from the users actively or passively. Publishing this data for research and other analytics without removing Personally Identifiable Information (PII) will lead to the privacy breach. Existing anonymization techniques are failing to maintain the balance between data privacy and data utility. In order to provide a trade-off between the privacy of the users and data utility, a Mondrian based k-anonymity approach is proposed. To protect the privacy of high-dimensional data Deep Neural Network (DNN) based framework is proposed. The experimental result shows that the proposed approach mitigates the information loss of the data without compromising privacy.
AB - In recent trends, privacy preservation is the most predominant factor, on big data analytics and cloud computing. Every organization collects personal data from the users actively or passively. Publishing this data for research and other analytics without removing Personally Identifiable Information (PII) will lead to the privacy breach. Existing anonymization techniques are failing to maintain the balance between data privacy and data utility. In order to provide a trade-off between the privacy of the users and data utility, a Mondrian based k-anonymity approach is proposed. To protect the privacy of high-dimensional data Deep Neural Network (DNN) based framework is proposed. The experimental result shows that the proposed approach mitigates the information loss of the data without compromising privacy.
UR - http://www.scopus.com/inward/record.url?scp=85067968185&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85067968185&partnerID=8YFLogxK
U2 - 10.1109/ICACCS.2019.8728384
DO - 10.1109/ICACCS.2019.8728384
M3 - Conference contribution
AN - SCOPUS:85067968185
T3 - 2019 5th International Conference on Advanced Computing and Communication Systems, ICACCS 2019
SP - 722
EP - 727
BT - 2019 5th International Conference on Advanced Computing and Communication Systems, ICACCS 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th International Conference on Advanced Computing and Communication Systems, ICACCS 2019
Y2 - 15 March 2019 through 16 March 2019
ER -