Privacy preserving classification on local differential privacy in data centers

Fan, Weibei; He, Jing; Guo, Mengjiao; Li, Peng; Han, Zhijie; Wang, Ruchuan

doi:10.1016/j.jpdc.2019.09.009

Cited by 54 publications

(22 citation statements)

References 20 publications

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“…We used the hg13 genome as the reference genome to compress the other 1101 genomes. We evaluated the performance of four second-level matching percentages of HRCM-B and Input: reference lowercase sequence: r lowercae [•]; reference lowercase sequence length: l r ; to-be-compressed lowercase sequence: t lowercae [•]; to-be-compressed lowercase sequence length: l t ; Initialize matched lowercase information: matched lowercase[•] � 0; start position of matching: start position � 1; index � 0 (1) for i � 0 to l t − 1 do (2) for j � start position to l r do (3) if t lowercase[i] � r lowercase[j] then (4) matched lowercase[i] � j (5) update start position � j + 1 (6) end if (7) end for (8) if matched lowercase[i] � 0 then (9) for j � start position − 1 to 1 do (10) if t lowercase[i] � r lowercase[j] then (11) matched lowercase[i] � j; (12) update start position � j + 1; (13) end if (14) end for (15) end if (16) if matched lowercase[i] � 0 then (17) mismatched lowercase[index] � t lowercase [i]; (18) update index � in dex + 1; (19) end if (20) end for Output: mismatched lowercase information; matched lowercase information;…”

Section: Compression Results and Analysis Of Homo Sapiensmentioning

confidence: 99%

“…(2) for i � 0 to n t − k do (3) Calculate the hash value value t i of the k-mer which starts from i in t seq; (4) pos (11) l � l + 1; (12) end while (13) if l max < l then (14) l max � l, pos max � p; (15) end if (16) update pos � L[pos]; (17) end while (18) end if (19) record the mismatched string to the mismatched information (20) record the matched string to the matched information the previous matched entity, and the storage space can be reduced by storing the difference between the real position and the predicted position. Finally, all the above-encoded information is encoded and stored using the PPMD encoder.…”

Section: Sequence Information Encodingmentioning

confidence: 99%

“…erefore, decompression is much faster and more resource-saved than compression. (12) len � len + 1; (13) end while (14) if len max < len then (15) len max � len, pos max � pos; (16) end if (17) update…”

Section: Decompressionmentioning

confidence: 99%

“…Genome sequences stored in the FASTA format are convenient for researchers to perform gene studies and sequence analysis. Many analysis methods for FASTA genomic data have been proposed, such as sequence alignment [13], sequence comparison [14], sequence factorization [15], and collection of k-mer statistics [16,17]. So FASTA format is widely used in genomic data storage.…”

Section: Introductionmentioning

confidence: 99%

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HRCM: An Efficient Hybrid Referential Compression Method for Genomic Big Data

Yao

et al. 2019

BioMed Research International

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With the maturity of genome sequencing technology, huge amounts of sequence reads as well as assembled genomes are generating. With the explosive growth of genomic data, the storage and transmission of genomic data are facing enormous challenges. FASTA, as one of the main storage formats for genome sequences, is widely used in the Gene Bank because it eases sequence analysis and gene research and is easy to be read. Many compression methods for FASTA genome sequences have been proposed, but they still have room for improvement. For example, the compression ratio and speed are not so high and robust enough, and memory consumption is not ideal, etc. Therefore, it is of great significance to improve the efficiency, robustness, and practicability of genomic data compression to reduce the storage and transmission cost of genomic data further and promote the research and development of genomic technology. In this manuscript, a hybrid referential compression method (HRCM) for FASTA genome sequences is proposed. HRCM is a lossless compression method able to compress single sequence as well as large collections of sequences. It is implemented through three stages: sequence information extraction, sequence information matching, and sequence information encoding. A large number of experiments fully evaluated the performance of HRCM. Experimental verification shows that HRCM is superior to the best-known methods in genome batch compression. Moreover, HRCM memory consumption is relatively low and can be deployed on standard PCs.

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Section: Compression Results and Analysis Of Homo Sapiensmentioning

confidence: 99%

Section: Sequence Information Encodingmentioning

confidence: 99%

Section: Decompressionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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HRCM: An Efficient Hybrid Referential Compression Method for Genomic Big Data

Yao

et al. 2019

BioMed Research International

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“…When considering the data center network (DCN), Fan et al [ 143 ] investigated the LDP-based support vector regression (SVR) classification for cloud computing supported data centers. Their method achieves LDP based on the Laplace mechanism.…”

Section: Machine Learning With Ldpmentioning

confidence: 99%

A Comprehensive Survey on Local Differential Privacy toward Data Statistics and Analysis

Wang

Zhang

Feng

et al. 2020

Sensors

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Collecting and analyzing massive data generated from smart devices have become increasingly pervasive in crowdsensing, which are the building blocks for data-driven decision-making. However, extensive statistics and analysis of such data will seriously threaten the privacy of participating users. Local differential privacy (LDP) was proposed as an excellent and prevalent privacy model with distributed architecture, which can provide strong privacy guarantees for each user while collecting and analyzing data. LDP ensures that each user’s data is locally perturbed first in the client-side and then sent to the server-side, thereby protecting data from privacy leaks on both the client-side and server-side. This survey presents a comprehensive and systematic overview of LDP with respect to privacy models, research tasks, enabling mechanisms, and various applications. Specifically, we first provide a theoretical summarization of LDP, including the LDP model, the variants of LDP, and the basic framework of LDP algorithms. Then, we investigate and compare the diverse LDP mechanisms for various data statistics and analysis tasks from the perspectives of frequency estimation, mean estimation, and machine learning. Furthermore, we also summarize practical LDP-based application scenarios. Finally, we outline several future research directions under LDP.

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Topology analysis and routing algorithms design for PTNet network

Han

Zhang

et al. 2020

Concurrency and Computation

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Summary Data center network (DCN) is used for transmission, storage, and processing of big data, which plays an important role in cloud computing and CDN distribution. Network topology and routing algorithm are its core research content and key technical issues. The traditional network topology is difficult to guarantee the quality of service in scalability and fault tolerance. The server‐centric DCN topology can ensure the scale of the DCN by recursively increasing the number of network nodes and links. Relative to the Dcell, BCube, and BCCC typical network topology, PTNet network as a typical representative of a new type of the server‐centric DCN topology, which has more advantages in scalability, fault tolerance, and so on. The PTNet network topology is theoretically analyzed in terms of network diameter, bottleneck throughput, and total number of links in the network. Based on the deep research of PTNet network, this article analyzes and studies the network topology, multicast, and broadcast routing algorithm.

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Privacy preserving classification on local differential privacy in data centers

Cited by 54 publications

References 20 publications

HRCM: An Efficient Hybrid Referential Compression Method for Genomic Big Data

HRCM: An Efficient Hybrid Referential Compression Method for Genomic Big Data

A Comprehensive Survey on Local Differential Privacy toward Data Statistics and Analysis

Topology analysis and routing algorithms design for PTNet network

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