Privacy preserving Back-propagation neural network learning over arbitrarily partitioned data

Bansal, Ankur; Chen, Tingting; Zhong, Sheng

doi:10.1007/s00521-010-0346-z

Cited by 76 publications

(40 citation statements)

References 15 publications

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“…al. [6] proposes a privacy conserving BPN network learning algorithmic program for 2 party situations. This scheme provides sturdy protection for knowledge sets as well as intermediate results.…”

Section: Proposed Systemmentioning

confidence: 99%

Privacy Preserving Back-Propagation Neural Network with Cloud

Vyavahare¹,

Khobragade²,

Wankhade³

et al. 2017

International Journal of Advanced Research in Computer and Comm

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Abstract:To improve the accuracy of learning result, in observe multiple parties might collaborate through conducting joint Back propagation neural network learning on the union of their several knowledge sets. throughout this method no party needs to disclose her/his non-public knowledge to others. Existing schemes supporting this type of cooperative learning are either restricted within the means of information partition or simply take into account 2 parties. There lacks an answer that enables two or a lot of parties, every with an at random partitioned off information set, to collaboratively conduct the training. This paper solves this open drawback by utilizing the ability of cloud computing. In our planned theme, every party encrypts his/her non-public knowledge regionally and uploads the ciphertexts into the cloud. By firmly offloading the high-ticket operations to the cloud, we tend to keep the computation and communication prices on every party nominal and freelance to the amount of participants. To support versatile operations over ciphertexts, we tend to adopt and tailor the BGN 'doubly homomorphic' coding algorithmic rule for the multi-party setting. Numerical analysis and experiments on goods cloud show that our theme is secure, economical and correct.

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Section: Proposed Systemmentioning

confidence: 99%

Privacy Preserving Back-Propagation Neural Network with Cloud

Vyavahare¹,

Khobragade²,

Wankhade³

et al. 2017

International Journal of Advanced Research in Computer and Comm

View full text Add to dashboard Cite

show abstract

“…In this paper, we consider arbitrarily partitioned data [4] among multi-parties, say Z parties(Z > 2). For arbitrarily partitioned data, each party P s , 1 ≤ s ≤ Z, holds parts of the data set without any specific order.…”

Section: Arbitrarily Partitioned Datamentioning

confidence: 99%

“…By securely outsource most computation tasks to the cloud server, our scheme makes the cost of each party independent to the number of participating parties. To compare our scheme with existing ones [4], [6], we summarize the cost of our scheme and Ref. [4], [6] in Table.1.…”

Section: Numerically Analysismentioning

confidence: 99%

“…al. [4] enhanced this scheme and proposed a solution for arbitrarily partitioned data. Nevertheless, this enhanced scheme, just like [6], was proposed for the two-party scenario.…”

Section: Introductionmentioning

confidence: 99%

“…In practical implementation, such a complexity represents a tremendous cost on each party considering the already expensive operations on the underlying groups such as Elliptic Curves. However, [4] just considers the two-party scenario though it supports arbitrarily partitioned dataset. To our best knowledge, none of existing schemes have solved all these challenges at the same time.…”

Section: Introductionmentioning

confidence: 99%

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Privacy Preserving Back-Propagation Neural Network Learning Made Practical with Cloud Computing

Yuan

2014

IEEE Trans. Parallel Distrib. Syst.

192

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Abstract-To improve the accuracy of learning result, in practice multiple parties may collaborate through conducting joint Backpropagation neural network learning on the union of their respective data sets. During this process no party wants to disclose her/his private data to others. Existing schemes supporting this kind of collaborative learning are either limited in the way of data partition or just consider two parties. There lacks a solution that allows two or more parties, each with an arbitrarily partitioned data set, to collaboratively conduct the learning. This paper solves this open problem by utilizing the power of cloud computing. In our proposed scheme, each party encrypts his/her private data locally and uploads the ciphertexts into the cloud. The cloud then executes most of the operations pertaining to the learning algorithms over ciphertexts without knowing the original private data. By securely offloading the expensive operations to the cloud, we keep the computation and communication costs on each party minimal and independent to the number of participants. To support flexible operations over ciphertexts, we adopt and tailor the BGN'doubly homomorphic' encryption algorithm for the multi-party setting. Numerical analysis and experiments on commodity cloud show that our scheme is secure, efficient and accurate.

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Heterogeneous differential privacy for vertically partitioned databases

Xia

Zhu

Ding

et al. 2019

Concurrency and Computation

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Summary Existing privacy‐preserving approaches are generally designed to provide privacy guarantee for individual data in a database, which reduces the utility of the database for data analysis. In this paper, we propose a novel differential privacy mechanism to preserve the heterogeneous privacy of a vertically partitioned database based on attributes. We first present the concept of privacy label, which characterizes the privacy information of the database and is instantiated by the classification. Then, we use an information‐based method to systematically explore the dependencies between all attributes and the privacy label. We finally assign privacy weights to every attribute and design a heterogeneous mechanism according to the basic Laplace mechanism. Evaluations using real datasets demonstrate that the proposed mechanism achieves a balanced privacy and utility.

show abstract

Privacy preserving Back-propagation neural network learning over arbitrarily partitioned data

Cited by 76 publications

References 15 publications

Privacy Preserving Back-Propagation Neural Network with Cloud

Privacy Preserving Back-Propagation Neural Network with Cloud

Privacy Preserving Back-Propagation Neural Network Learning Made Practical with Cloud Computing

Heterogeneous differential privacy for vertically partitioned databases

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