PRIVACY PRESERVING BACK-PROPOGATION NEURAL NETWORK LEARNING MADE PRACTICAL WITH CLOUD COMPUTING

WELCOME

PRESENTED BY,THUSHARA.M

M.Tech CSISROLL NO:18

PRIVACY PRESERVING BACK-PROPOGATION NEURAL NETWORK LEARNING IN CLOUD

COMPUTING

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IntroductionLiterature reviewContributionsModels and assumptionsTechnique preliminariesProposed schemePerformance evaluationConclusionReferences

CONTENTS

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Neural networks.Back-propogation.Improves the accuracy.Joint/Collaborative learning.

INTRODUCTION

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Challenges: To protect each participant’s private data set and

intermediate results.

The computation/ communication cost introduced to each participant shall be affordable.

For collaborative training, training data is arbitrarily partitioned.

INTRODUCTION(Contd..)

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Provides privacy preservation for multiparty .

Collaborative BPN network learning over arbitrarily partitioned data.

Guarantees privacy and efficiency.

Support multiparty secure scalar product.

Allow decryption of arbitrary large messages.

CONTRIBUTONS

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System Model: Trusted authority. The participating parties ( data owner). The cloud servers ( or cloud).

Security Model:

MODELS AND ASSUMPTIONS

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Arbitrarily Partitioned Data Z parties (Z > 2 ) : Ps , 1 ≤ s ≤ Z. Database D with N rows : {DB1,DB2, ….. DBN}. Each row DBv ,1 ≤ v ≤ N has m attributes {xv

1 , xv2 , xv

3 …..

xvm}.

DBv = DBv1 U DBv

2 U DBv3 U ….. U DBv

z .

Each DBv, Ps has tsv attributes.

TECHNIQUE PRELIMINARIES

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BACK –PROPOGATION NEURAL NETWORK LEARNING

TECHNIQUE PRELIMINARIES(Contd..)

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BGN Homomorphic Encryption Operations on plaintexts to be performed on their

respective cipher texts. Public-key “doubly homomorphic” encryption

scheme(called “BGN” for short). One multiplication and unlimited number of additions. Given ciphertexts C(m1) , C(m2) and C(m^1), C(m^2 ), one

can compute C(m1 m^1 + m2m^2) without knowing the plaintext.

TECHNIQUE PRELIMINARIES(Contd..)

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PROBLEM STATEMENT 3 layer (a-b-c configuration) neural network . N samples for learning data set . Arbitrary partitioned into Z( Z≥2) subsets.

SCHEME OVERVIEW Each party encrypt her/his input data set. Participants upload the encrypted data to cloud. Cloud servers perform the operations. Secret sharing algorithm.

PROPOSED SCHEME

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PRIVACY PRESERVING MULTIPARTY NEURAL NETWORK LEARNING

PROPOSED SCHEME(Contd..)

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PROPOSED SCHEME(Contd..)

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SECURE SCALAR PRODUCTION AND ADDITION WITH CLOUD

Algorithm 3: Secure Scalar Product and Addition Key Generation. Encryption. Secure Scalar Product. Secure Addition. Decryption.

PROPOSED SCHEME(Contd..)

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SECURE SHARING OF SCALAR PRODUCT AND SUM

PROPOSED SCHEME(Contd..)

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APPROXIMATION OF ACTIVATION FUNCTION

PROPOSED SCHEME(Contd..)

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Experimental Evaluation Experiment Setup• Amazon EC2 cloud.• 10 nodes with 8-core 2.93-GHz Intel Xeon CPU.• 8-GB memory.• Testing data sets(Iris,kr-vs-kp,diabetes).

PERFORMANCE EVALUATION

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EXPERIMENTAL RESULT

PERFORMANCE EVALUATION(Contd..)

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EXPERIMENTAL RESULT (Contd..)

PERFORMANCE EVALUATION(Contd..)

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EXPERIMENTAL RESULT (Contd..)

PERFORMANCE EVALUATION(Contd..)

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EXPERIMENTAL RESULT (Contd..)

PERFORMANCE EVALUATION(Contd..)

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EXPERIMENTAL RESULT (Contd..)

PERFORMANCE EVALUATION(Contd..)

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EXPERIMENTAL RESULT (Contd..)

PERFORMANCE EVALUATION(Contd..)

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EXPERIMENTAL RESULT (Contd..)

PERFORMANCE EVALUATION(Contd..)

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ACCURACY ANALYSIS Accuracy loss in approximation of activation function. Maclaurin series used – accuracy can be adjusted by

modifying number of series terms.

PERFORMANCE EVALUATION(Contd..)

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Secure and practical multiparty BPN network learning.

Cost independent of number of parties.

Scalable efficient and secure.

CONCLUSION

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1) N. Schlitter A Protocol for Privacy Preserving Neural Network Learning on Horizontal Partitioned Data, Proc. Privacy Statistics in Databases (PSD ’08), Sept. 20082) T. Chen and S. Zhong, Privacy-Preserving Backpropagation Neural Network Learning,IEEE Trans. Neural Network, vol. 20, no. 10, Oct. 2000,pp. 1554-15643) A. Bansal, T. Chen, and S. Zhong, Privacy Preserving Back-Propagation Neural Network Learning over Arbitrarily Parti-tioned Data,Neural Computing Applications,vol. 20, no. 1, Feb. 2011, pp. 143-150, 4) D. Boneh, E.-J. Goh, and K. Nissim, Evaluating 2-DNF Formulas on Ciphertexts,Proc. Second Int’l Conf. Theory of Cryptography (TCC ’05), pp. 325-341, 2005.

REFERENCES

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