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

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

  • Introduction Literature review Contributions Models and assumptions Technique preliminaries Proposed scheme Performance evaluation Conclusion References 3 CONTENTS
  • Neural networks. Back-propogation. Improves the accuracy. Joint/Collaborative learning. 4 INTRODUCTION
  • 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. 5 INTRODUCTION(Contd..)
  • 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. 6 CONTRIBUTONS
  •  System Model:  Trusted authority.  The participating parties ( data owner).  The cloud servers ( or cloud).  Security Model: 7 MODELS AND ASSUMPTIONS
  •  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 , xv 2 , xv 3 ….. xv m}.  DBv = DBv 1 U DBv 2 U DBv 3 U ….. U DBv z .  Each DBv, Ps has ts v attributes. 8 TECHNIQUE PRELIMINARIES
  •  BACK –PROPOGATION NEURAL NETWORK LEARNING 9 TECHNIQUE PRELIMINARIES(Contd..)
  •  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. 10 TECHNIQUE PRELIMINARIES(Contd..)
  •  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. 11 PROPOSED SCHEME
  •  PRIVACY PRESERVING MULTIPARTY NEURAL NETWORK LEARNING 12 PROPOSED SCHEME(Contd..)
  • 13 PROPOSED SCHEME(Contd..)
  •  SECURE SCALAR PRODUCTION AND ADDITION WITH CLOUD Algorithm 3: Secure Scalar Product and Addition  Key Generation.  Encryption.  Secure Scalar Product.  Secure Addition.  Decryption. 14 PROPOSED SCHEME(Contd..)
  •  SECURE SHARING OF SCALAR PRODUCT AND SUM 15 PROPOSED SCHEME(Contd..)
  •  APPROXIMATION OF ACTIVATION FUNCTION 16 PROPOSED SCHEME(Contd..)
  •  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). 17 PERFORMANCE EVALUATION
  •  EXPERIMENTAL RESULT 18 PERFORMANCE EVALUATION(Contd..)
  •  EXPERIMENTAL RESULT (Contd..) 19 PERFORMANCE EVALUATION(Contd..)
  •  EXPERIMENTAL RESULT (Contd..) 20 PERFORMANCE EVALUATION(Contd..)
  •  EXPERIMENTAL RESULT (Contd..) 21 PERFORMANCE EVALUATION(Contd..)
  •  EXPERIMENTAL RESULT (Contd..) 22 PERFORMANCE EVALUATION(Contd..)
  •  EXPERIMENTAL RESULT (Contd..) 23 PERFORMANCE EVALUATION(Contd..)
  •  EXPERIMENTAL RESULT (Contd..) 24 PERFORMANCE EVALUATION(Contd..)
  •  ACCURACY ANALYSIS  Accuracy loss in approximation of activation function.  Maclaurin series used – accuracy can be adjusted by modifying number of series terms. 25 PERFORMANCE EVALUATION(Contd..)
  • Secure and practical multiparty BPN network learning. Cost independent of number of parties. Scalable efficient and secure. 26 CONCLUSION
  • 1) N. Schlitter A Protocol for Privacy Preserving Neural Network Learning on Horizontal Partitioned Data, Proc. Privacy Statistics in Databases (PSD ’08), Sept. 2008 2) T. Chen and S. Zhong, Privacy-Preserving Backpropagation Neural Network Learning,IEEE Trans. Neural Network, vol. 20, no. 10, Oct. 2000,pp. 1554-1564 3) 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. 27 REFERENCES
  • THANK YOU