EmpiricalRiskMinimizationDP.CMS: Privacy-preserving Empirical Risk Minimization for Binary...

In DPpack: Differentially Private Statistical Analysis and Machine Learning

Privacy-preserving Empirical Risk Minimization for Binary Classification

Description

This class implements differentially private empirical risk minimization \insertCitechaudhuri2011DPpack. Either the output or the objective perturbation method can be used. It is intended to be a framework for building more specific models via inheritance. See LogisticRegressionDP for an example of this type of structure.

Details

To use this class for empirical risk minimization, first use the new method to construct an object of this class with the desired function values and hyperparameters. After constructing the object, the fit method can be applied with a provided dataset and data bounds to fit the model. In fitting, the model stores a vector of coefficients coeff which satisfy differential privacy. These can be released directly, or used in conjunction with the predict method to privately predict the outcomes of new datapoints.

Note that in order to guarantee differential privacy for empirical risk minimization, certain constraints must be satisfied for the values used to construct the object, as well as for the data used to fit. These conditions depend on the chosen perturbation method. Specifically, the provided loss function must be convex and differentiable with respect to y.hat, and the absolute value of the first derivative of the loss function must be at most 1. If objective perturbation is chosen, the loss function must also be doubly differentiable and the absolute value of the second derivative of the loss function must be bounded above by a constant c for all possible values of y.hat and y, where y.hat is the predicted label and y is the true label. The regularizer must be 1-strongly convex and differentiable. It also must be doubly differentiable if objective perturbation is chosen. Finally, it is assumed that if x represents a single row of the dataset X, then the l2-norm of x is at most 1 for all x. Note that because of this, a bias term cannot be included without appropriate scaling/preprocessing of the dataset. To ensure privacy, the add.bias argument in the fit and predict methods should only be utilized in subclasses within this package where appropriate preprocessing is implemented, not in this class.

Public fields

mapXy

Map function of the form mapXy(X, coeff) mapping input data matrix X and coefficient vector or matrix coeff to output labels y.

mapXy.gr

Function representing the gradient of the map function with respect to the values in coeff and of the form mapXy.gr(X, coeff), where X is a matrix and coeff is a matrix or numeric vector.

loss

Loss function of the form loss(y.hat, y), where y.hat and y are matrices.

loss.gr

Function representing the gradient of the loss function with respect to y.hat and of the form loss.gr(y.hat, y), where y.hat and y are matrices.

regularizer

Regularization function of the form regularizer(coeff), where coeff is a vector or matrix.

regularizer.gr

Function representing the gradient of the regularization function with respect to coeff and of the form regularizer.gr(coeff).

gamma

Nonnegative real number representing the regularization constant.

eps

Positive real number defining the epsilon privacy budget. If set to Inf, runs algorithm without differential privacy.

perturbation.method

String indicating whether to use the 'output' or the 'objective' perturbation methods \insertCitechaudhuri2011DPpack.

c

Positive real number denoting the upper bound on the absolute value of the second derivative of the loss function, as required to ensure differential privacy for the objective perturbation method.

coeff

Numeric vector of coefficients for the model.

kernel

Value only used in child class svmDP. String indicating which kernel to use for SVM. Must be one of {'linear', 'Gaussian'}. If 'linear' (default), linear SVM is used. If 'Gaussian', uses the sampling function corresponding to the Gaussian (radial) kernel approximation.

D

Value only used in child class svmDP. Nonnegative integer indicating the dimensionality of the transform space approximating the kernel. Higher values of D provide better kernel approximations at a cost of computational efficiency.

sampling

Value only used in child class svmDP. Sampling function of the form sampling(d), where d is the input dimension, returning a (d+1)-dimensional vector of samples corresponding to the Fourier transform of the kernel to be approximated.

phi

Value only used in child class svmDP. Function of the form phi(x, theta), where x is an individual row of the original dataset, and theta is a (d+1)-dimensional vector sampled from the Fourier transform of the kernel to be approximated, where d is the dimension of x. The function returns a numeric scalar corresponding to the pre-filtered value at the given row with the given sampled vector.

kernel.param

Value only used in child class svmDP. Positive real number corresponding to the Gaussian kernel parameter.

prefilter

Value only used in child class svmDP. Matrix of pre-filter values used in converting data into transform space.

Methods

Public methods

• EmpiricalRiskMinimizationDP.CMS$new()

• EmpiricalRiskMinimizationDP.CMS$fit()

• EmpiricalRiskMinimizationDP.CMS$predict()

• EmpiricalRiskMinimizationDP.CMS$clone()

---

Method new()

Create a new EmpiricalRiskMinimizationDP.CMS object.

Usage

EmpiricalRiskMinimizationDP.CMS$new(
  mapXy,
  loss,
  regularizer,
  eps,
  gamma,
  perturbation.method = "objective",
  c = NULL,
  mapXy.gr = NULL,
  loss.gr = NULL,
  regularizer.gr = NULL
)

Arguments

mapXy

Map function of the form mapXy(X, coeff) mapping input data matrix X and coefficient vector or matrix coeff to output labels y. Should return a column matrix of predicted labels for each row of X. See mapXy.sigmoid for an example.

loss

Loss function of the form loss(y.hat, y), where y.hat and y are matrices. Should be defined such that it returns a matrix of loss values for each element of y.hat and y. See loss.cross.entropy for an example. It must be convex and differentiable, and the absolute value of the first derivative of the loss function must be at most 1. Additionally, if the objective perturbation method is chosen, it must be doubly differentiable and the absolute value of the second derivative of the loss function must be bounded above by a constant c for all possible values of y.hat and y.

regularizer

String or regularization function. If a string, must be 'l2', indicating to use l2 regularization. If a function, must have form regularizer(coeff), where coeff is a vector or matrix, and return the value of the regularizer at coeff. See regularizer.l2 for an example. Additionally, in order to ensure differential privacy, the function must be 1-strongly convex and differentiable. If the objective perturbation method is chosen, it must also be doubly differentiable.

eps

Positive real number defining the epsilon privacy budget. If set to Inf, runs algorithm without differential privacy.

gamma

Nonnegative real number representing the regularization constant.

perturbation.method

String indicating whether to use the 'output' or the 'objective' perturbation methods \insertCitechaudhuri2011DPpack. Defaults to 'objective'.

c

Positive real number denoting the upper bound on the absolute value of the second derivative of the loss function, as required to ensure differential privacy for the objective perturbation method. This input is unnecessary if perturbation.method is 'output', but is required if perturbation.method is 'objective'. Defaults to NULL.

mapXy.gr

Optional function representing the gradient of the map function with respect to the values in coeff. If given, must be of the form mapXy.gr(X, coeff), where X is a matrix and coeff is a matrix or numeric vector. Should be defined such that the ith row of the output represents the gradient with respect to the ith coefficient. See mapXy.gr.sigmoid for an example. If not given, non-gradient based optimization methods are used to compute the coefficient values in fitting the model.

loss.gr

Optional function representing the gradient of the loss function with respect to y.hat and of the form loss.gr(y.hat, y), where y.hat and y are matrices. Should be defined such that the ith row of the output represents the gradient of the loss function at the ith set of input values. See loss.gr.cross.entropy for an example. If not given, non-gradient based optimization methods are used to compute the coefficient values in fitting the model.

regularizer.gr

Optional function representing the gradient of the regularization function with respect to coeff and of the form regularizer.gr(coeff). Should return a vector. See regularizer.gr.l2 for an example. If regularizer is given as a string, this value is ignored. If not given and regularizer is a function, non-gradient based optimization methods are used to compute the coefficient values in fitting the model.

Returns

A new EmpiricalRiskMinimizationDP.CMS object.

---

Method fit()

Fit the differentially private empirical risk minimization model. This method runs either the output perturbation or the objective perturbation algorithm \insertCitechaudhuri2011DPpack, depending on the value of perturbation.method used to construct the object, to generate an objective function. A numerical optimization method is then run to find optimal coefficients for fitting the model given the training data and hyperparameters. The built-in optim function using the "BFGS" optimization method is used. If mapXy.gr, loss.gr, and regularizer.gr are all given in the construction of the object, the gradient of the objective function is utilized by optim as well. Otherwise, non-gradient based optimization methods are used. The resulting privacy-preserving coefficients are stored in coeff.

Usage

EmpiricalRiskMinimizationDP.CMS$fit(
  X,
  y,
  upper.bounds,
  lower.bounds,
  add.bias = FALSE
)

Arguments

X

Dataframe of data to be fit.

y

Vector or matrix of true labels for each row of X.

upper.bounds

Numeric vector of length ncol(X) giving upper bounds on the values in each column of X. The ncol(X) values are assumed to be in the same order as the corresponding columns of X. Any value in the columns of X larger than the corresponding upper bound is clipped at the bound.

lower.bounds

Numeric vector of length ncol(X) giving lower bounds on the values in each column of X. The ncol(X) values are assumed to be in the same order as the corresponding columns of X. Any value in the columns of X larger than the corresponding upper bound is clipped at the bound.

add.bias

Boolean indicating whether to add a bias term to X. Defaults to FALSE.

---

Method predict()

Predict label(s) for given X using the fitted coefficients.

Usage

EmpiricalRiskMinimizationDP.CMS$predict(X, add.bias = FALSE)

Arguments

X

Dataframe of data on which to make predictions. Must be of same form as X used to fit coefficients.

add.bias

Boolean indicating whether to add a bias term to X. Defaults to FALSE. If add.bias was set to TRUE when fitting the coefficients, add.bias should be set to TRUE for predictions.

Returns

Matrix of predicted labels corresponding to each row of X.

---

Method clone()

The objects of this class are cloneable with this method.

Usage

EmpiricalRiskMinimizationDP.CMS$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

References

\insertRef

chaudhuri2011DPpack

Examples

# Build train dataset X and y, and test dataset Xtest and ytest
N <- 200
K <- 2
X <- data.frame()
y <- data.frame()
for (j in (1:K)){
  t <- seq(-.25, .25, length.out = N)
  if (j==1) m <- stats::rnorm(N,-.2, .1)
  if (j==2) m <- stats::rnorm(N, .2, .1)
  Xtemp <- data.frame(x1 = 3*t , x2 = m - t)
  ytemp <- data.frame(matrix(j-1, N, 1))
  X <- rbind(X, Xtemp)
  y <- rbind(y, ytemp)
}
Xtest <- X[seq(1,(N*K),10),]
ytest <- y[seq(1,(N*K),10),,drop=FALSE]
X <- X[-seq(1,(N*K),10),]
y <- y[-seq(1,(N*K),10),,drop=FALSE]

# Construct object for logistic regression
mapXy <- function(X, coeff) e1071::sigmoid(X%*%coeff)
# Cross entropy loss
loss <- function(y.hat,y) -(y*log(y.hat) + (1-y)*log(1-y.hat))
regularizer <- 'l2' # Alternatively, function(coeff) coeff%*%coeff/2
eps <- 1
gamma <- 1
perturbation.method <- 'objective'
c <- 1/4 # Required value for logistic regression
mapXy.gr <- function(X, coeff) as.numeric(e1071::dsigmoid(X%*%coeff))*t(X)
loss.gr <- function(y.hat, y) -y/y.hat + (1-y)/(1-y.hat)
regularizer.gr <- function(coeff) coeff
ermdp <- EmpiricalRiskMinimizationDP.CMS$new(mapXy, loss, regularizer, eps,
                                             gamma, perturbation.method, c,
                                             mapXy.gr, loss.gr,
                                             regularizer.gr)

# Fit with data
# Bounds for X based on construction
upper.bounds <- c( 1, 1)
lower.bounds <- c(-1,-1)
ermdp$fit(X, y, upper.bounds, lower.bounds) # No bias term
ermdp$coeff # Gets private coefficients

# Predict new data points
predicted.y <- ermdp$predict(Xtest)
n.errors <- sum(round(predicted.y)!=ytest)

DPpack documentation built on Oct. 20, 2024, 9:07 a.m.