Privacy preservation in data publishing is the major topic of research in the field of data security. Data publication in privacy preservation provides methodologies for publishing useful information; simultaneously the privacy of the sensitive data has to be preserved. This work can handle any number of sensitive attributes. The major security breaches are membership, identity and attribute disclosure. In this paper, a novel approach based on slicing that adheres to the principle of k-anonymity and l-diversity is introduced. The proposed work withstands all the privacy threats by the incorporation of k-means and cuckoo-search algorithm. The experimental results with respect to suppression ratio, execution time and information loss are satisfactory, when compared with the existing approaches.
IshwaryaM.V. and KumarK.R., Selective Colligation and Selective Scrambling for Privacy Preservation in Data Mining, Indonesian Journal of Electrical Engineering and Computer Science10(2) (2018), 778–785.
2.
KwakyeM.M., Privacy-preservation in data pre-processing for web usage mining, International Journal of Information Privacy, Security and Integrity4(2) (2019), 134–152.
3.
XuL., JiangC., ChenY., WangJ. and RenY., A framework for categorizing and applying privacy-preservation techniques in big data mining, Computer49(2) (2016), 54–62.
4.
ChamikaraM.A.P., BertokP., LiuD., CamtepeS. and KhalilI., Efficient privacy preservation of big data for accurate data mining, Information Sciences (2019).
5.
MandalaJ. and RaoM.C.S., Privacy preservation of data using crow search with adaptive awareness probability, Journal of Information Security and Applications44 (2019), 157–169.
6.
FungB.C., WangK. and PhilipS.Y., Anonymizing classification data for privacy preservation, IEEE Transactions on Knowledge and Data Engineering19(5) (2007), 711–725.
7.
WangQ., ZhangY., LuX., WangZ., QinZ. and RenK., Real-time and spatio-temporal crowd-sourced social network data publishing with differential privacy, IEEE Transactions on Dependable and Secure Computing15(4) (2016), 591–606.
8.
BhaduriK., StefanskiM.D. and SrivastavaA.N., Privacy-preserving outlier detection through random nonlinear data distortion, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)41(1) (2010), 260–272.
9.
WangJ., DengC. and LiX., Two privacy-preserving approaches for publishing transactional data streams, IEEE Access6 (2018), 23648–23658.
10.
ZhangL., LiuY., WangR., FuX. and LinQ., Efficient privacy-preserving classification construction model with differential privacy technology, Journal of Systems Engineering and Electronics28(1) (2017), 170–178.
11.
LiuC., DuanH., QingtianZ.E.N.G., ZhouM., LuF. and ChengJ., Towards comprehensive support for privacy preservation cross-organization business process mining, IEEE Transactions on Services Computing (2016).
12.
JiaQ., GuoL., JinZ. and FangY., Preserving model privacy for machine learning in distributed systems, IEEE Transactions on Parallel and Distributed Systems29(8) (2018), 1808–1822.
13.
ZhangX., YangL.T., LiuC. and ChenJ., A scalable two-phase top-down specialization approach for data anonymization using mapreduce on cloud, IEEE Transactions on Parallel and Distributed Systems25(2) (2013), 363–373.
14.
DongX., ChenJ., ZhangK. and QianH., Privacy-Preserving Locally Weighted Linear Regression over Encrypted Millions of Data, IEEE Access. (2019).
15.
TerrovitisM., PoulisG., MamoulisN. and SkiadopoulosS., Local suppression and splitting techniques for privacy preserving publication of trajectories, IEEE Transactions on Knowledge and Data Engineering29(7) (2017), 1466–1479.
16.
LiJ., WeiJ., YeM., LiuW. and HuX., Privacy-preserving constrained spectral clustering algorithm for large-scale data sets, IET Information Security14(3) (2019), 321–331.
17.
ZhouJ., CaoZ., DongX. and LinX., PPDM: A privacy-preserving protocol for cloud-assisted e-healthcare systems, IEEE Journal of Selected Topics in Signal Processing9(7) (2015), 1332–1344.
18.
YangX., LuR., ShaoJ., TangX. and YangH., An Efficient and Privacy-Preserving Disease Risk Prediction Scheme for E-Healthcare, IEEE Internet of Things Journal6(2) (2018), 3284–3297.
19.
DingX., LiuP. and JinH., Privacy-Preserving Multi-Keyword Top-$ k $ k Similarity Search Over Encrypted Data, IEEE Transactions on Dependable and Secure Computing16(2) (2017), 344–357.
20.
AliA., et al., Medicines Selection via Fuzzy Upward B-covering Rough Sets, Journal of Intelligent & Fuzzy Systems37(3) (2019), 4369–4390.
21.
HuangA., Similarity Measures for Text Document Clustering, In Proceedings of the sixth New Zealand computer science research student conference (NZCSRSC2008), Christchurch, New Zealand, (2008), pp. 49–56.
22.
LiuJ. and KeH., Impact of Social Media Retailing on Pricing Decisions Under Uncertain Environment, Journal of Intelligent & Fuzzy Systems37(4) (2019), 5515–5529.
Xiao-chunY., Ya-zheW. and BinW., Privacy Preserving Approaches for Multiple Sensitive Attributes in Data Publishing, Chinese Journal of Computers31(04) (2008), 574–587.
