Differential identifiability clustering algorithms for big data analysis

Shang, Tao; Zhao, Zheng; Ren, Xujie; Liu, Jian‐Wei

doi:10.1007/s11432-020-2910-1

Cited by 7 publications

(7 citation statements)

References 35 publications

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“…e scheduling queue allocates computing tasks to the corresponding computing server nodes and monitors the operation status of each computing task at the same time. If the task fails, the scheduling queue will recall the unfinished task and assign it to other computing server nodes for execution [19].…”

Section: Parallel Optimization Processing Strategy For the Convolutio...mentioning

confidence: 99%

Big Data Analysis of Marketing User Intelligence Information Based on Deep Learning

Shen

2022

Mobile Information Systems

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To solve the problem that the explosive growth of data cannot be effectively analyzed, a big data analysis and prediction system based on deep learning is proposed. The systems proposed in this paper optimize the design of circulatory neural networks using an optimization processing strategy in addition to circulatory neural networks based on a distributed parallel processing framework that studies and analyzes disrupted neural networks during the in-depth study. The results are as follows: using the DCNNPS parallel strategy can effectively improve the training speed of the convolutional neural network to a small extent, and the speed can be improved by more than 50%. Compared with the other two, the network optimized by DCNNPS is less sensitive to data growth in the scenario of a large amount of data and is more suitable for processing a large amount of data. Moreover, with the increase of computing nodes, the acceleration effect is more obvious, the acceleration ratio is higher, and the improvement energy is about 70%. It is proved that the system proposed in this paper can process massive data very effectively and make a great contribution to the development of the industry.

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Section: Parallel Optimization Processing Strategy For the Convolutio...mentioning

confidence: 99%

Big Data Analysis of Marketing User Intelligence Information Based on Deep Learning

Shen

2022

Mobile Information Systems

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“…Compared with differential privacy, the privacy parameter setting of differential differentiability is more intuitive and easier to be understood by relevant practitioners. Shang et al [23] proposed the composition theorem of differential identifiability and applied it to the k-means clustering in the MapReduce framework.…”

Section: Related Workmentioning

confidence: 99%

“…For data publishing in some complex situations, privacy protection mechanisms may need to be applied multiple times. On the basis of Lee and Clifton's work, Shang et al [23] studied the composition theorem of differential identifiability. Based on the composition theorem, the differential identifiability privacy protection with mathematical proof can be provided when the adversary has the ability of multiple combinatorial queries.…”

Section: Definition 1 (ρ-Differential Identifiability)mentioning

confidence: 99%

Gene Sequence Clustering Based on the Profile Hidden Markov Model with Differential Identifiability

Ren

Shang

Jiang

et al. 2021

Security and Communication Networks

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In the era of big data, next-generation sequencing produces a large amount of genomic data. With these genetic sequence data, research in biology fields will be further advanced. However, the growth of data scale often leads to privacy issues. Even if the data is not open, it is still possible for an attacker to steal private information by a member inference attack. In this paper, we proposed a private profile hidden Markov model (PHMM) with differential identifiability for gene sequence clustering. By adding random noise into the model, the probability of identifying individuals in the database is limited. The gene sequences could be unsupervised clustered without labels according to the output scores of private PHMM. The variation of the divergence distance in the experimental results shows that the addition of noise makes the profile hidden Markov model distort to a certain extent, and the maximum divergence distance can reach 15.47 when the amount of data is small. Also, the cosine similarity comparison of the clustering model before and after adding noise shows that as the privacy parameters changes, the clustering model distorts at a low or high level, which makes it defend the member inference attack.

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“…Whole transcriptome sequencing, based on cDNA sequence sequencing, can detect information about the overall transcriptional activity [4]. Targeted target sequencing can select some genes required for disease research for higher sequencing efficiency, but it is not suitable for detecting unknown mutations [5][6][7]. The techniques of experimental manipulation (wet experiment) and bioinformatics analysis (dry experiment) have been developed continuously.…”

Section: Introductionmentioning

confidence: 99%

Big Data Analysis and Application of Liver Cancer Gene Sequence Based on Second-Generation Sequencing Technology

Xiao

Wang

Jia

et al. 2022

Computational and Mathematical Methods in Medicine

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In big data analysis with the rapid improvement of computer storage capacity and the rapid development of complex algorithms, the exponential growth of massive data has also made science and technology progress with each passing day. Based on omics data such as mRNA data, microRNA data, or DNA methylation data, this study uses traditional clustering methods such as kmeans, K-nearest neighbors, hierarchical clustering, affinity propagation, and nonnegative matrix decomposition to classify samples into categories, obtained: (1) The assumption that the attributes are independent of each other reduces the classification effect of the algorithm to a certain extent. According to the idea of multilevel grid, there is a one-to-one mapping from high-dimensional space to one-dimensional. The complexity is greatly simplified by encoding the one-dimensional grid of the hierarchical grid. The logic of the algorithm is relatively simple, and it also has a very stable classification efficiency. (2) Convert the two-dimensional representation of the data into the one-dimensional representation of the binary, realize the dimensionality reduction processing of the data, and improve the organization and storage efficiency of the data. The grid coding expresses the spatial position of the data, maintains the original organization method of the data, and does not make the abstract expression of the data object. (3) The data processing of nondiscrete and missing values provides a new opportunity for the identification of protein targets of small molecule therapy and obtains a better classification effect. (4) The comparison of the three models shows that Naive Bayes is the optimal model. Each iteration is composed of alternately expected steps and maximal steps and then identified and quantified by MS.

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Differential identifiability clustering algorithms for big data analysis

Cited by 7 publications

References 35 publications

Big Data Analysis of Marketing User Intelligence Information Based on Deep Learning

Big Data Analysis of Marketing User Intelligence Information Based on Deep Learning

Gene Sequence Clustering Based on the Profile Hidden Markov Model with Differential Identifiability

Big Data Analysis and Application of Liver Cancer Gene Sequence Based on Second-Generation Sequencing Technology

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