Deep Learning with Data Privacy via Residual Perturbation

1 INTRODUCTION . Many high-capacity deep nets ( DNs ) are trained with private data , including medical images and financial transaction data ( Yuen et al. , 2011 ; Feng et al. , 2017 ; Liu et al. , 2017 ) . DNs usually overfit and can memorize the private training data , which makes training DNs exposed to data privacy leakage ( Fredrikson et al. , 2015a ; Shokri et al. , 2017 ; Salem et al. , 2018 ; Yeom et al. , 2018 ; Sablayrolles et al. , 2018 ) . Given a pre-trained DN , the membership inference attack can determine if an instance is in the training set based on DN ’ s response ( Fredrikson et al. , 2014 ; Shokri et al. , 2017 ; Salem et al. , 2018 ) ; the model extraction attack can learn a surrogate model that matches the target model , given the adversary only black-box access to the target model ( Tramèr et al. , 2016 ; Gong & Liu , 2018 ) ; the model inversion attack can infer certain features of a given input from the output of a target model ( Fredrikson et al. , 2015b ; Al-Rubaie & Chang , 2016 ) ; the attribute inference attack can deanonymize the anonymized training data ( Gong & Liu , 2016 ; Zheng et al. , 2018 ) . Machine learning ( ML ) with data privacy is crucial in many applications ( Lindell & Pinkas , 2000 ; Barreno et al. , 2006 ; Hesamifard et al. , 2018 ; Bae et al. , 2019 ) . Several algorithms have been developed to reduce privacy leakage include differential privacy ( DP ) ( Dwork et al. , 2006 ) , federated learning ( FL ) ( McMahan et al. , 2016 ; Konečnỳ et al. , 2016 ) , and k-anonymity ( Sweeney , 2002 ; El Emam & Dankar , 2008 ) . Objective , output , and gradient perturbations are among the most used approaches for ML with DP guarantees at the cost of significant utility degradation ( Chaudhuri et al. , 2011 ; Bassily et al. , 2014 ; Shokri & Shmatikov , 2015 ; Abadi et al. , 2016b ; Bagdasaryan et al. , 2019 ) . FL trains centralized ML models , through gradient exchange , with the training data being distributed at the edge devices . However , the gradient exchange can still leak the privacy ( Zhu et al. , 2019 ; Wang et al. , 2019c ) . Most of the existing privacy is achieved at a tremendous sacrifice of utility . Moreover , training ML models using the state-of-the-art DP stochastic gradient descent ( DPSGD ) leads to tremendous computational cost due to the requirement of computing and clipping the per-sample gradient ( Abadi et al. , 2016a ) . It remains a great interest to develop new privacy-preserving ML algorithms without excessive computational overhead or degrading the utility of the ML models . 1.1 OUR CONTRIBUTION . In this paper , we propose residual perturbation for privacy-preserving deep learning ( DL ) with DP guarantees . At the core of residual perturbation is injecting Gaussian noise to each residual mapping of ResNet ( He et al. , 2016 ) , and the residual perturbation is theoretically principled by the stochastic differential equation ( SDE ) theory . The major advantages of residual perturbation are threefold : • It can protect the membership privacy of the training data almost perfectly and often without sacrificing ResNets ’ utility . Furthermore , it can even improve ResNets ’ classification accuracy . • It has fewer hyperparameters to tune than the benchmark DPSGD . Also , it is more computationally efficient than DPSGD , which requires to compute the per-sample gradient . • It can be easily implemented by a few lines of code in modern DL libraries . 1.2 RELATED WORK . Improving the utility of ML models with DP guarantees is an important task . PATE ( Papernot et al. , 2017 ; 2018 ) uses semi-supervised learning together with model transfer between the “ student ” and “ teacher ” models to enhance utility . Several variants of the DP notions have also been proposed to improve the privacy budget and some times can also improve the resulting model ’ s utility at a given DP budget ( Abadi et al. , 2016b ; Mironov , 2017 ; Wang et al. , 2018 ; Dong et al. , 2019 ) . Some post-processing techniques have also been developed to improve the utility of ML models with negligible computational overhead ( Wang et al. , 2019a ; Liang et al. , 2020 ) . From the SDE viewpoint , ( Li et al. , 2019 ; Wang et al. , 2015 ) showed that several stochastic gradient Monte Carlo samplers could reach state-of-the-art performance in terms of both privacy and utility in Bayesian learning . Gaussian noise injection in residual learning has been used to improve the robustness of ResNets ( Rakin et al. , 2018 ; Wang et al. , 2019b ; Liu et al. , 2019 ) . In this paper , we inject Gaussian noise to each residual mapping to achieve data privacy instead of adversarial robustness . 1.3 ORGANIZATION . We organize this paper as follows : In Section 2 , we introduce the residual perturbation for privacypreserving DL . In Section 3 , we present the generalization and DP guarantees for residual perturbation . In Section 4 , we numerically verify the efficiency of the residual perturbation in protecting data privacy without degrading the underlying models ’ utility . We end with some concluding remarks . Technical proofs and some more experimental details and results are provided in the appendix . 1.4 NOTATIONS . We denote scalars by lower or upper case letters ; vectors/ matrices by lower/upper case bold face letters . For a vector x = ( x1 , · · · , xd ) ∈ Rd , we use ‖x‖2 = ( ∑d i=1 |xi|2 ) 1/2 to denote its ` 2 norm . For a matrix A , we use ‖A‖2 to denote its induced norm by the vector ` 2 norm . We denote the standard Gaussian in Rd as N ( 0 , I ) with I ∈ Rd×d being the identity matrix . The set of ( positive ) real numbers is denoted as ( R+ ) R. We use B ( 0 , R ) to denote the ball centered at 0 with radius R . 2 ALGORITHMS . 2.1 DEEP RESIDUAL LEARNING AND ITS CONTINUOUS ANALOGUE . Given the training set SN : = { xi , yi } Ni=1 , with { xi , yi } ⊂ Rd × R being a data-label pair . For a given xi the forward propagation of a ResNet with M residual mappings can be written as xl+1 = xl + F̂ ( xl , Wl ) , for l = 0 , 1 , · · · , M − 1 , with x0 = xi ; ŷi = f ( xM ) , ( 1 ) where F̂ ( · , Wl ) is the nonlinear mapping of the lth residual mapping parameterized by Wl ; f is the output activation function , and ŷi is the predicted label for xi . The heuristic continuum limit of ( 1 ) is dx ( t ) = F ( x ( t ) , W ( t ) ) dt , x ( 0 ) = x̂ , where t is the time variable . ( 2 ) The ordinary differential equation ( ODE ) ( 2 ) can be revertible , and thus the ResNet counterpart might be exposed to data privacy leakage . For instance , we use the ICLR logo ( Fig . 1 ( a ) ) as the initial data x̂ in ( 2 ) . Then we simulate the forward propagation of ResNet by solving ( 2 ) from t = 0 to t = 1 using the forward Euler solver with a time step size ∆t = 0.01 and a given velocity field F ( x ( t ) , W ( t ) ) ( see Appendix E for the details of F ( x ( t ) , W ( t ) ) ) , which maps the original image to its features ( Fig . 1 ( b ) ) . To recover the original image , we start from the feature and use the backward Euler iteration , i.e. , x̃ ( t ) = x̃ ( t+∆t ) −∆tF ( x̃ ( t+∆t ) , t+∆t ) , to evolve x̃ ( t ) from t = 1 to t = 0 with x̃ ( 1 ) = x ( 1 ) being the features obtained in the forward propagation . We plot the recovered image from features in Fig . 1 ( c ) , and the original image can be almost perfectly recovered . 2.2 RESIDUAL PERTURBATION AND ITS SDE ANALOGUE . In this part , we propose two SDE models to reduce the reversibility of ( 2 ) , and the corresponding residual perturbations analogue can protect the data privacy in DL . Strategy I . For the first strategy , we consider the following SDE model : dx ( t ) = F ( x ( t ) , W ( t ) ) dt+ γdB ( t ) , γ > 0 , ( 3 ) where B ( t ) is the standard Brownian motion . We simulate the forward propagation and reverseengineering the input from the output by solving the SDE model ( 3 ) with γ = 1 using the same F ( x ( t ) , W ( t ) ) and initial data x̂ . We use the following forward ( 4 ) and backward ( 5 ) EulerMaruyama discretizations ( Higham , 2001 ) of ( 3 ) , x ( t+ ∆t ) = x ( t ) + ∆tF ( x ( t ) , W ( t ) ) + γN ( 0 , √ ∆t I ) , ( 4 ) x̃ ( t ) = x̃ ( t+ ∆t ) −∆tF ( x̃ ( t+ ∆t ) , W ( t+ ∆t ) ) + γN ( 0 , √ ∆t I ) , ( 5 ) for the forward and backward propagation , respectively . Figure 1 ( d ) and ( e ) show the results of the forward and backward propagation by SDE , respectively , and these results show that it is much harder to reverse the features obtained by SDE evolution . The SDE model informs us to inject Gaussian noise , in both training and test phases , to each residual mapping of ResNet to protect data privacy , which results in xi+1 = xi + F̂ ( xi , Wi ) + γni , where ni ∼ N ( 0 , I ) 1 . ( 6 ) Strategy II . For the second strategy , we consider using the multiplicative noise instead of the additive noise used in ( 3 ) 2 and ( 6 ) , and the corresponding SDE can be written as dx ( t ) = F ( x ( t ) , W ( t ) ) dt+ γx ( t ) dB ( t ) , γ > 0 , ( 7 ) where denotes the Hadamard product . Similarly , we can use the forward and backward EulerMaruyama discretizations of ( 7 ) to propagate the image in Fig . 1 ( a ) , and we provide these results in Appendix D.1 . The corresponding residual perturbation is xi+1 = xi + F̂ ( xi , Wi ) + γxi ni , where ni ∼ N ( 0 , I ) , ( 8 ) again , the noise γxi ni is injected to each residual mapping in both training and test phases . We will provide theoretical guarantees for these two residual perturbation schemes , i.e. , ( 6 ) and ( 8 ) , in Section 3 , and numerically verify their efficacy in Section 4 .

Summary: This paper studies an important problem and proposes the novel residual perturbation to protect privacy while maintaining the ResNet models’ utility.  Two SDE models are provided to inject noises with abundant theoretical proof are provided. Experimental results demonstrate the performance of privacy protection and classification accuracy on benchmark datasets. My major concern is about the utility enhancement and the DP guarantee (see cons below). Hope the authors can address my concern in the rebuttal period.