3 research outputs found
Hybrid Cloud-Based Privacy Preserving Clustering as Service for Enterprise Big Data
Clustering as service is being offered by many cloud service providers. It helps enterprises to learn hidden patterns and learn knowledge from large, big data generated by enterprises. Though it brings lot of value to enterprises, it also exposes the data to various security and privacy threats. Privacy preserving clustering is being proposed a solution to address this problem. But the privacy preserving clustering as outsourced service model involves too much overhead on querying user, lacks adaptivity to incremental data and involves frequent interaction between service provider and the querying user. There is also a lack of personalization to clustering by the querying user. This work “Locality Sensitive Hashing for Transformed Dataset (LSHTD)” proposes a hybrid cloud-based clustering as service model for streaming data that address the problems in the existing model such as privacy preserving k-means clustering outsourcing under multiple keys (PPCOM) and secure nearest neighbor clustering (SNNC) models, The solution combines hybrid cloud, LSHTD clustering algorithm as outsourced service model. Through experiments, the proposed solution is able is found to reduce the computation cost by 23% and communication cost by 6% and able to provide better clustering accuracy with ARI greater than 4.59% compared to existing works
NegDL: Privacy-Preserving Deep Learning Based on Negative Database
In the era of big data, deep learning has become an increasingly popular
topic. It has outstanding achievements in the fields of image recognition,
object detection, and natural language processing et al. The first priority of
deep learning is exploiting valuable information from a large amount of data,
which will inevitably induce privacy issues that are worthy of attention.
Presently, several privacy-preserving deep learning methods have been proposed,
but most of them suffer from a non-negligible degradation of either efficiency
or accuracy. Negative database (\textit{NDB}) is a new type of data
representation which can protect data privacy by storing and utilizing the
complementary form of original data. In this paper, we propose a
privacy-preserving deep learning method named NegDL based on \textit{NDB}.
Specifically, private data are first converted to \textit{NDB} as the input of
deep learning models by a generation algorithm called \textit{QK}-hidden
algorithm, and then the sketches of \textit{NDB} are extracted for training and
inference. We demonstrate that the computational complexity of NegDL is the
same as the original deep learning model without privacy protection.
Experimental results on Breast Cancer, MNIST, and CIFAR-10 benchmark datasets
demonstrate that the accuracy of NegDL could be comparable to the original deep
learning model in most cases, and it performs better than the method based on
differential privacy