CN110443063B - Adaptive privacy-protecting federal deep learning method - Google Patents
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Abstract
The invention provides a federal deep learning method for self-adaptive privacy protection, which is used for protecting the original data of a user in the federal deep learning from being known by a curious server and protecting the parameters of a learning model from leaking the information of the original data of the user. Each participant negotiates a network frame with a cloud server in advance, then the cloud server obtains an initialized model, and the cloud server broadcasts the model parameters to each participant; the method comprises the following steps that participants download initialized model parameters and update local models of the participants, then training is carried out by combining local data sets, different privacy protection operations are carried out on different data characteristics based on different contribution degrees of data attributes to model output, and the participants send local gradients obtained by respective training to a cloud server; finally, the cloud server updates the model of each participant after collecting the gradient information of each participant to perform subsequent training. The invention greatly improves the accuracy of the learning model on the premise of meeting the privacy protection.
Description
Technical Field
The invention relates to an artificial intelligence technology.
Background
Traditional centralized deep learning requires that user data is concentrated in one data center, and a user loses control over own data, and the data can be abused or guessed by a data user to obtain more private information of the user. Joint Deep Learning (fed Deep Learning) proposed by Google can solve the problems of privacy, location, right of use, etc. of user data.
Joint Deep Learning (fed Deep Learning) allows multiple participants to jointly learn a common model without disclosing their own data sets. This requires the participants to train with their own local data sets to obtain a local model and share their respective training gradients with other participants; the cloud server or a user aggregates the training gradients from the participants and then derives a "common" model that also prevents local overfitting of the user's local model.
A Differential Privacy Mechanism (Differential Privacy Mechanism) is a cryptographic technique commonly used in statistics, and is used to remove individual features to protect the Privacy of user data on the premise of keeping the statistical features of the data. Laplace mechanisms are commonly used to implement E-differential privacy. Specifically, it is to inject laplacian distribution compliant noise into the data item such that it satisfies differential privacy with privacy budget e. The larger the privacy budget, the higher the level of privacy protection. In the actual use process, the serial and parallel combination characteristics of differential privacy are combined frequently, so that the application of differential privacy is more flexible.
At present, there is a privacy protection mechanism based on technologies such as secure multi-party computation, homomorphic encryption, differential privacy and the like. However, considering the future data growth trend, the differential privacy mechanism has good efficiency performance compared with the secure multi-party calculation which sacrifices communication overhead and the homomorphic encryption which requires calculation overhead. However, the differential privacy mechanism requires a trade-off between data privacy and model accuracy.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a federal deep learning method which can ensure the accuracy of a training model and the high efficiency in a large-scale user scene and simultaneously prevent a server from deducing the model parameters and the self-adaptability of the privacy of user training data.
The technical scheme adopted by the invention for solving the technical problems is that the federal deep learning method for adaptively protecting privacy comprises the following steps:
1) Initializing a system: each participant negotiates a deep learning model with a cloud server, and then the cloud server selects public data of the same type as user data as training data to train the deep learning model to obtain a model parameter w of the deep learning model global (ii) a The training data comprises a plurality of data items and corresponding labels, and the data items are composed of a plurality of data attributes;
2) The participants initialize the local model: the server stores the model parameters w of the deep learning model global Broadcast to participants who download the model parameters w global As the initial model parameters of the local deep learning model;
3) The participants update the deep learning model with local user data:
3-1) calculating the contribution degree of the data item to the output of the model by utilizing a layer-by-layer correlation propagation algorithm;
3-2) aggregating and averaging the contribution degrees of the data attributes of the same data attribute class to obtain the contribution degree of the data attribute class;
3-3) carrying out differential privacy protection on the contribution degree: injecting Laplace noise into the contribution degree of the data attribute class, so that the contribution degree of the data attribute class after the Laplace noise is injected meets the privacy budget epsilon of the contribution degree c Differential privacy of (1);
4) Participants run a small batch gradient descent algorithm to optimize the local model:
4-1) the participant selects a plurality of data items and corresponding labels as training data;
4-2) carrying out differential privacy protection on data attributes: adaptively injecting Laplace noise into the data attribute according to the contribution degree of the data attribute class where the data attribute is located, wherein the injected Laplace noise is positively correlated with the contribution degree of the data attribute class where the data attribute is located, and the data attribute after the injection of Laplace noise meets the privacy budget epsilon of the data attribute l The difference of (2) is private;
4-3) carrying out differential privacy protection on the labels of the training data attributes: expanding a loss function into a polynomial form through Taylor, and then injecting Laplacian noise into a polynomial coefficient to enable the loss function to meet a label privacy budget epsilon f Differential privacy of (1); contribution privacy budget epsilon c Data attribute privacy budget ε l And tag privacy budget ε f The sum of the three is a preset total privacy budget;
4-4) calculating the gradient of the model by derivation of a loss function, and updating the local model;
4-5) uploading the model gradient to a cloud server by a participant;
5) Cloud server aggregation: the cloud server collects the model gradient sent by each participant to update the global model on the cloud server.
By combining the federal deep learning and differential privacy technology and using a layer-by-layer correlation propagation algorithm, a user can calculate the contribution of data attributes to the output of the model; participants can use a random privacy protection adjustment technique to self-define the privacy level of the data, and then adaptively inject laplacian noise into the data attributes of the user according to the contribution degree. For the data attribute with small contribution degree, a large noise value is used for disturbance, and the privacy of the system is improved; for the data attribute with larger contribution degree, small noise is injected, and the accuracy of the model can be effectively increased.
The invention has the beneficial effect that the accuracy of the system is improved to the maximum extent on the premise of ensuring the privacy protection level of the system.
Drawings
FIG. 1 is a schematic diagram of a system;
FIG. 2 is a schematic diagram of a layer-by-layer correlation propagation algorithm.
Detailed Description
1. The system model of the invention is shown in figure 1:
2. system initialization, comprising the steps of:
1) Participant U g The participants U need to be jointly found to be a more accurate learning model that does not result in local overfitting g A deep learning network is negotiated with the cloud server in advance, such as a Convolutional Neural Network (CNN), a Recurrent Neural Network (RNN) and the like;
2) The server trains by using some public data according to the user data type to obtain an initialized deep learning model parameter w global ；
3. The participant initializes the local model, characterized by comprising the steps of:
1) The server broadcasts its own deep learning model parameters w global ；
2) Each participant U g Downloading initialized model parameters w global And updating the local learning model w of the user local ；
4. Participant preprocessing of local data, characterized by the following steps:
1) Data normalization: each participant U g Having a local data set D g G is a data set sequence number variable, data set D g In which n data items x are included i And a corresponding label y i Each data item comprising u data attributes x i，j Composition of：x i，1 ，x i，2 ，...，x i，u (ii) a Label y i ∈[1，v]Data item x i The middle 1 data attribute corresponds to 1 data attribute type; i is an element of [1, n ]]，j∈[1，u]Through the data normalization operation, the range of the data attribute value can be limited:
this operation can speed up the training;
2) Through a layer-by-layer dependency propagation algorithm, a participant can compute a data attribute x in a single data item i，j Contribution to model output: input layer l input Number of neurons on (Input layer) and data attribute x i，j The same number, layer-by-layer correlation propagation is shown in fig. 2.
With initialized local model parameters w local And local data set
The predicted result y = f (x) of a model is obtained by feedforward network training i ). As can be seen, data item x i Through the output layer l o Superior neuron a b Degree of contribution to the output of the model pick>
As output value f (x) of the model i ) B and c are variables representing the neuron numbers:
then the output value is transmitted back layer by layer, and the data attribute xi can be calculated through the linear relation between network layers k Upper neuron a c To the k-1 st layer l k-1 Neuron a b Degree of contribution between
The following:
w b，c to connect neurons a b And neuron a c The weight of the other(s) between,
is a neuron a b Mu is a number close to 0, where 10 is taken -6 ；
From the above equation, the data item x can be calculated i Neurons a through the k-th layer b Degree of contribution to model output
For data item x i Neuron a through the k-th layer b Connected neurons a on the k +1 th layer c ∈l k+1 The sum of the contribution degrees. The method comprises the following specific steps:
3) The participants calculate the contribution degree of the data attribute class to the output of the model: input layer l input Number of upper neurons and data attributes x i，j The number is the same, and one neuron corresponds to one data attribute (one data attribute class). And the participants can calculate the contribution degree of the data attribute class by combining the contribution degrees of the data attributes of the same data attribute class in the n data items. Since the neuron of the input layer functions to convert received data (pictures, sounds, etc.) into numerical values, the input layer l input Neuron a of b The output of (2) represents the data attribute x i，j When is coming into contact with
Input layer l input Number of upper neurons and data attribute x i，j B is equal to [1, u ] if the number is the same]. Extracting input layers when calculating contribution of data attributes according to a layer-by-layer propagation algorithmRepresents the contribution size of the data attribute class. The participator combines the contribution degree of the same data attribute class in the n data items to calculate the contribution degree C of each data attribute class j j . The method comprises the following specific steps: />
4) Privacy preserving operations on contribution calculation process: in the process of calculating the contribution degree, attribute classes which are more important to the output of the learning model can be distinguished. In order to protect the original data of the user from being leaked or speculated, a differential privacy mechanism is adopted to disturb the contribution degree, namely: laplacian noise is injected into the contribution to the data attribute class. The method comprises the following specific steps:
wherein Lap represents the probability density function of Laplace distribution, GS c The sensitivity of the preset data attribute class to contribution degree of the model output reflects the maximum difference of data between adjacent data set pairs, and the sensitivity is a fixed value under a determined neural network structure; epsilon c Is a privacy budget of contribution degree, and the larger value of the privacy budget indicates that the noise value is smaller, which leads to higher system accuracy and provides weaker privacy protection level. The probability density function of the laplace distribution is:
a is a scale parameter, order
||·|| 1 Is 1-norm;
5. the participant trains the local model, including the following steps:
1) The participant selects t data tuples: at each timeDuring training, the participants U g Randomly selecting local data sets
In small-batch data sets with a number t>
As training data of this time;
2) For data attribute x i，j Privacy protection is carried out: with a random privacy preserving adjustment technique, we introduce two adjustment factors: f, p. Wherein f is a user-defined threshold value, which can define the privacy level of the user; p is a probability value. The contribution ratio is:
if the contribution ratio of the user attribute class is greater than the threshold, the attribute class contribution is defined to be greater. To improve the privacy level of the model, laplace noise is injected into all attributes of this type:
and defining the user attribute with the contribution ratio smaller than the threshold value f as a small contribution, and performing probabilistic noise injection on the attribute to improve the accuracy of the model. The method comprises the following specific steps:
wherein the way of injecting the noise is adaptive. Let the privacy budget ε of each data attribute class j Comprises the following steps:
ε j ＝β*ε l
the privacy budget ε j Privacy budget for data attributes ε l And distributing according to the contribution ratio. The adaptivity injects the following noise:
wherein GS is l Is the data attribute sensitivity.
3) Label y for training data i Privacy protection is carried out: the protection for the label is realized by injecting Laplace noise into a loss function; in round r training, when we choose sigmoid function:
the taylor expansion in combination with a cross-entropy (cross-entropy) cost function as an activation function for neurons is: />
Where, two parts in the expression representing the cross entropy cost function, y i A tag indicating the ith data item, k a variable indicating the tag type number, v the total number of classes of the tag, F 1，k (z)＝y i *log(1+e -z ))，F 2，k (z)＝(1-y i )* log(1+e -z )，
In (2), 0 represents a variable of 0, and superscripts (0), (1) and (2) represent 0, 1 and 2 derivations, respectively. And, in conjunction with>
Processing data items x for a neural network i When the output vector of the last layer of the hidden layer is processed, due to the characteristics of the neural network, the input vector of the layer is the output vector of the previous layer except the input layer.
To protect labels y of training data i For polynomial coefficient
4) Optimizing a local model, and improving the accuracy of the system: calculating model gradients by derivation of a loss function
Let η be the learning rate of the local model, and update the local model:
5) Uploading gradient information by participants: order:
participant send vector
Sending the data to a cloud server;
6. the cloud server aggregation comprises the following steps:
the cloud server receives gradient information vectors sent by all parties, and updates a local model: let eta be global Learning rates for the server learning model. Root of herbaceous plantsThe model is updated according to the following formula:
in summary, the invention provides a federal deep learning method with adaptive privacy protection, which can protect the original data of a user in the federal deep learning from being known by a curious server and protect the parameters of a learning model from leaking the information of the original data of the user.
Claims (2)
1. An adaptive privacy-preserving federal deep learning method is characterized by comprising the following steps:
1) Initializing a system: each participant negotiates a deep learning model with a cloud server, and then the cloud server selects public data of the same type as user data as training data to train the deep learning model to obtain a model parameter w of the deep learning model global (ii) a The training data comprises a plurality of data items and corresponding labels, and the data items are composed of a plurality of data attributes;
2) The participants initialize the local model: the server stores the model parameters w of the deep learning model global Broadcast to participants who download the model parameters w global As the initialization model parameters of the local deep learning model;
3) The participants update the deep learning model with local user data:
3-1) calculating the contribution degree of the data item to the output of the model by utilizing a layer-by-layer correlation propagation algorithm;
3-2) aggregating and averaging the contribution degrees of the data attributes of the same data attribute class to obtain the contribution degree of the data attribute class;
3-3) carrying out differential privacy protection on the contribution degree: injecting Laplace noise into the contribution degree of the data attribute class, so that the contribution degree of the data attribute class after the Laplace noise is injected meets the privacy budget epsilon of the contribution degree c Differential privacy of (1);
4) Participants run a small batch gradient descent algorithm to optimize the local model:
4-1) the participant selects a plurality of data items and corresponding labels as training data;
4-2) carrying out differential privacy protection on data attributes: according to the contribution degree of the data attribute class where the data attribute is located, the Laplace noise is adaptively injected into the data attribute, the injected Laplace noise is positively correlated with the contribution degree of the data attribute class where the data attribute is located, and the data attribute after the Laplace noise is injected meets the privacy budget epsilon of the data attribute l Differential privacy of (1);
the specific method for adaptively injecting the laplacian noise into the data attribute according to the contribution degree of the data attribute class where the data attribute is located is as follows:
calculating the contribution ratio beta of the data attribute class, and injecting Laplace noise into all the data attributes in the data attribute class when the contribution ratio of the data attribute class is larger than or equal to a preset threshold value; when the contribution ratio of the data attribute class is smaller than a preset threshold value, injecting Laplace noise into the data attribute class according to a preset probability; the noise injection mode is that the privacy budget corresponding to the data attribute class is allocated to the data attribute, and the privacy budget of the data attribute class is the privacy budget epsilon of the data attribute l Multiplying the result of the contribution ratio of the data attribute class; the contribution ratio of the data attribute class is: the ratio of the absolute value of the contribution of the data attribute class after the laplacian noise is injected to the absolute value of the contribution of all the data attribute classes after the laplacian noise is injected is 4-3) the label of the training data attribute is subjected to differential privacy protection: expanding a loss function into a polynomial form through Taylor, and then injecting Laplacian noise into a polynomial coefficient to enable the loss function to meet a label privacy budget epsilon f Differential privacy of (1); contribution privacy budget epsilon c Data attribute privacy budget ε l And sign privacy budget ε f The sum of the three is a preset total privacy budget;
4-4) calculating the gradient of the model by derivation of a loss function, and updating the local model;
4-5) uploading the model gradient to a cloud server by a participant;
5) Cloud server aggregation: the cloud server collects the model gradient sent by each participant to update the global model on the cloud server.
2. The method of claim 1, wherein step 3-1) is preceded by a data normalization process on the data attributes.
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