privacy preserving back-propogation neural network learning made practical with cloud computing
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TRANSCRIPT
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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