EmpiricalRiskMinimizationDP.KST: Privacy-preserving Empirical Risk Minimization for Regression
In DPpack: Differentially Private Statistical Analysis and Machine Learning
EmpiricalRiskMinimizationDP.KST R Documentation
Privacy-preserving Empirical Risk Minimization for Regression
Description
This class implements differentially private empirical risk
minimization using the objective perturbation technique
\insertCiteKifer2012DPpack. It is intended to be a framework for
building more specific models via inheritance. See
LinearRegressionDP 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 the empirical risk
minimization model, certain constraints must be satisfied for the values
used to construct the object, as well as for the data used to fit.
Specifically, the following constraints must be met. Let l represent
the loss function for an individual dataset row x and output value y and
L represent the average loss over all rows and output values. First,
L must be convex with a continuous Hessian. Second, the l2-norm of the
gradient of l must be bounded above by some constant zeta for all
possible input values in the domain. Third, for all possible inputs to
l, the Hessian of l must be of rank at most one and its
Eigenvalues must be bounded above by some constant lambda. Fourth, the
regularizer must be convex. Finally, the provided domain of l must be
a closed convex subset of the set of all real-valued vectors of dimension p,
where p is the number of columns of 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
mapXyMap function of the form mapXy(X, coeff) mapping input
data matrix X and coefficient vector or matrix coeff to
output labels y.
mapXy.grFunction 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.
lossLoss function of the form loss(y.hat, y), where
y.hat and y are matrices.
loss.grFunction 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.
regularizerRegularization function of the form
regularizer(coeff), where coeff is a vector or matrix.
regularizer.grFunction representing the gradient of the
regularization function with respect to coeff and of the form
regularizer.gr(coeff).
gammaNonnegative real number representing the regularization
constant.
epsPositive real number defining the epsilon privacy budget. If set
to Inf, runs algorithm without differential privacy.
deltaNonnegative real number defining the delta privacy parameter.
If 0, reduces to pure eps-DP.
domainList of constraint and jacobian functions representing the
constraints on the search space for the objective perturbation algorithm
used in \insertCiteKifer2012;textualDPpack.
zetaPositive real number denoting the upper bound on the l2-norm
value of the gradient of the loss function, as required to ensure
differential privacy.
lambdaPositive real number corresponding to the upper bound of the
Eigenvalues of the Hessian of the loss function for all possible inputs.
coeffNumeric vector of coefficients for the model.
Methods
Public methods
-
-
-
-
Method new()
Create a new EmpiricalRiskMinimizationDP.KST object.
Usage
EmpiricalRiskMinimizationDP.KST$new(
mapXy,
loss,
regularizer,
eps,
delta,
domain,
zeta,
lambda,
gamma,
mapXy.gr = NULL,
loss.gr = NULL,
regularizer.gr = NULL
)
Arguments
mapXyMap 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.linear for an example.
lossLoss 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.squared.error for an example. This
function must be convex and the l2-norm of its gradient must be bounded
above by zeta for some constant zeta for all possible inputs within the
given domain. Additionally, for all possible inputs within the given
domain, the Hessian of the loss function must be of rank at most one and
its Eigenvalues must be bounded above by some constant lambda.
regularizerString 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 convex.
epsPositive real number defining the epsilon privacy budget. If set
to Inf, runs algorithm without differential privacy.
deltaNonnegative real number defining the delta privacy parameter.
If 0, reduces to pure eps-DP.
domainList of functions representing the constraints on the search
space for the objective perturbation algorithm. Must contain two
functions, labeled "constraints" and "jacobian", respectively. The
"constraints" function accepts a vector of coefficients from the search
space and returns a value such that the value is nonpositive if and only
if the input coefficient vector is within the constrained search space.
The "jacobian" function also accepts a vector of coefficients and returns
the Jacobian of the constraint function. For example, in linear
regression, the square of the l2-norm of the coefficient vector
\theta is assumed to be bounded above by p, where p is the length
of \theta \insertCiteKifer2012DPpack. So, domain could be
defined as domain <- list("constraints"=function(coeff) coeff%*%coeff-length(coeff), "jacobian"=function(coeff) 2*coeff).
zetaPositive real number denoting the upper bound on the l2-norm
value of the gradient of the loss function, as required to ensure
differential privacy.
lambdaPositive real number corresponding to the upper bound of the
Eigenvalues of the Hessian of the loss function for all possible inputs.
gammaNonnegative real number representing the regularization
constant.
mapXy.grOptional 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.linear for an example. If not
given, non-gradient based optimization methods are used to compute the
coefficient values in fitting the model.
loss.grOptional 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.squared.error for an example. If not given,
non-gradient based optimization methods are used to compute the
coefficient values in fitting the model.
regularizer.grOptional 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.KST object.
Method fit()
Fit the differentially private emprirical risk minimization
model. The function runs the objective perturbation algorithm
\insertCiteKifer2012DPpack 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
nloptr function 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 and the Jacobian of the constraint
function are utilized for the algorithm, and the NLOPT_LD_MMA method is
used. If one or more of these gradient functions are not given, the
NLOPT_LN_COBYLA method is used. The resulting privacy-preserving
coefficients are stored in coeff.
Usage
EmpiricalRiskMinimizationDP.KST$fit(
X,
y,
upper.bounds,
lower.bounds,
add.bias = FALSE
)
Arguments
XDataframe of data to be fit.
yVector or matrix of true values for each row of X.
upper.boundsNumeric vector of length ncol(X)+1 giving upper
bounds on the values in each column of X and the values of
y. The last value in the vector is assumed to be the upper bound
on y, while the first ncol(X) values are assumed to be in
the same order as the corresponding columns of X. Any value in the
columns of X and in y larger than the corresponding upper
bound is clipped at the bound.
lower.boundsNumeric vector of length ncol(X)+1 giving lower
bounds on the values in each column of X and the values of
y. The last value in the vector is assumed to be the lower bound
on y, while the first ncol(X) values are assumed to be in
the same order as the corresponding columns of X. Any value in the
columns of X and in y larger than the corresponding lower
bound is clipped at the bound.
add.biasBoolean indicating whether to add a bias term to X.
Defaults to FALSE.
Method predict()
Predict y values for given X using the fitted coefficients.
Usage
EmpiricalRiskMinimizationDP.KST$predict(X, add.bias = FALSE)
Arguments
XDataframe of data on which to make predictions. Must be of same
form as X used to fit coefficients.
add.biasBoolean indicating whether to add a bias term to X.
Defaults to FALSE. If add.bias was set to TRUE when fitting the
coefficients, add.bias should be set to TRUE for predictions.
Returns
Matrix of predicted y values corresponding to each row of X.
Method clone()
The objects of this class are cloneable with this method.
Usage
EmpiricalRiskMinimizationDP.KST$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
References
\insertRefKifer2012DPpack
Examples
# Build example dataset
n <- 500
X <- data.frame(X=seq(-1,1,length.out = n))
true.theta <- c(-.3,.5) # First element is bias term
p <- length(true.theta)
y <- true.theta[1] + as.matrix(X)%*%true.theta[2:p] + stats::rnorm(n=n,sd=.1)
# Construct object for linear regression
mapXy <- function(X, coeff) X%*%coeff
loss <- function(y.hat, y) (y.hat-y)^2/2
regularizer <- 'l2' # Alternatively, function(coeff) coeff%*%coeff/2
eps <- 1
delta <- 1
domain <- list("constraints"=function(coeff) coeff%*%coeff-length(coeff),
"jacobian"=function(coeff) 2*coeff)
# Set p to be the number of predictors desired including intercept term (length of coeff)
zeta <- 2*p^(3/2) # Proper bound for linear regression
lambda <- p # Proper bound for linear regression
gamma <- 1
mapXy.gr <- function(X, coeff) t(X)
loss.gr <- function(y.hat, y) y.hat-y
regularizer.gr <- function(coeff) coeff
ermdp <- EmpiricalRiskMinimizationDP.KST$new(mapXy, loss, 'l2', eps, delta,
domain, zeta, lambda,
gamma, mapXy.gr, loss.gr,
regularizer.gr)
# Fit with data
# We must assume y is a matrix with values between -p and p (-2 and 2
# for this example)
upper.bounds <- c(1, 2) # Bounds for X and y
lower.bounds <- c(-1,-2) # Bounds for X and y
ermdp$fit(X, y, upper.bounds, lower.bounds, add.bias=TRUE)
ermdp$coeff # Gets private coefficients
# Predict new data points
# Build a test dataset
Xtest <- data.frame(X=c(-.5, -.25, .1, .4))
predicted.y <- ermdp$predict(Xtest, add.bias=TRUE)
DPpack documentation built on Oct. 20, 2024, 9:07 a.m.
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| EmpiricalRiskMinimizationDP.KST | R Documentation |
Privacy-preserving Empirical Risk Minimization for Regression
Description
This class implements differentially private empirical risk
minimization using the objective perturbation technique
\insertCiteKifer2012DPpack. It is intended to be a framework for
building more specific models via inheritance. See
LinearRegressionDP 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 the empirical risk
minimization model, certain constraints must be satisfied for the values
used to construct the object, as well as for the data used to fit.
Specifically, the following constraints must be met. Let l represent
the loss function for an individual dataset row x and output value y and
L represent the average loss over all rows and output values. First,
L must be convex with a continuous Hessian. Second, the l2-norm of the
gradient of l must be bounded above by some constant zeta for all
possible input values in the domain. Third, for all possible inputs to
l, the Hessian of l must be of rank at most one and its
Eigenvalues must be bounded above by some constant lambda. Fourth, the
regularizer must be convex. Finally, the provided domain of l must be
a closed convex subset of the set of all real-valued vectors of dimension p,
where p is the number of columns of 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
mapXyMap function of the form
mapXy(X, coeff)mapping input data matrixXand coefficient vector or matrixcoeffto output labelsy.mapXy.grFunction representing the gradient of the map function with respect to the values in
coeffand of the formmapXy.gr(X, coeff), whereXis a matrix andcoeffis a matrix or numeric vector.lossLoss function of the form
loss(y.hat, y), wherey.hatandyare matrices.loss.grFunction representing the gradient of the loss function with respect to
y.hatand of the formloss.gr(y.hat, y), wherey.hatandyare matrices.regularizerRegularization function of the form
regularizer(coeff), wherecoeffis a vector or matrix.regularizer.grFunction representing the gradient of the regularization function with respect to
coeffand of the formregularizer.gr(coeff).gammaNonnegative real number representing the regularization constant.
epsPositive real number defining the epsilon privacy budget. If set to Inf, runs algorithm without differential privacy.
deltaNonnegative real number defining the delta privacy parameter. If 0, reduces to pure eps-DP.
domainList of constraint and jacobian functions representing the constraints on the search space for the objective perturbation algorithm used in \insertCiteKifer2012;textualDPpack.
zetaPositive real number denoting the upper bound on the l2-norm value of the gradient of the loss function, as required to ensure differential privacy.
lambdaPositive real number corresponding to the upper bound of the Eigenvalues of the Hessian of the loss function for all possible inputs.
coeffNumeric vector of coefficients for the model.
Methods
Public methods
Method new()
Create a new EmpiricalRiskMinimizationDP.KST object.
Usage
EmpiricalRiskMinimizationDP.KST$new( mapXy, loss, regularizer, eps, delta, domain, zeta, lambda, gamma, mapXy.gr = NULL, loss.gr = NULL, regularizer.gr = NULL )
Arguments
mapXyMap function of the form
mapXy(X, coeff)mapping input data matrixXand coefficient vector or matrixcoeffto output labelsy. Should return a column matrix of predicted labels for each row ofX. SeemapXy.linearfor an example.lossLoss function of the form
loss(y.hat, y), wherey.hatandyare matrices. Should be defined such that it returns a matrix of loss values for each element ofy.hatandy. Seeloss.squared.errorfor an example. This function must be convex and the l2-norm of its gradient must be bounded above by zeta for some constant zeta for all possible inputs within the given domain. Additionally, for all possible inputs within the given domain, the Hessian of the loss function must be of rank at most one and its Eigenvalues must be bounded above by some constant lambda.regularizerString or regularization function. If a string, must be 'l2', indicating to use l2 regularization. If a function, must have form
regularizer(coeff), wherecoeffis a vector or matrix, and return the value of the regularizer atcoeff. Seeregularizer.l2for an example. Additionally, in order to ensure differential privacy, the function must be convex.epsPositive real number defining the epsilon privacy budget. If set to Inf, runs algorithm without differential privacy.
deltaNonnegative real number defining the delta privacy parameter. If 0, reduces to pure eps-DP.
domainList of functions representing the constraints on the search space for the objective perturbation algorithm. Must contain two functions, labeled "constraints" and "jacobian", respectively. The "constraints" function accepts a vector of coefficients from the search space and returns a value such that the value is nonpositive if and only if the input coefficient vector is within the constrained search space. The "jacobian" function also accepts a vector of coefficients and returns the Jacobian of the constraint function. For example, in linear regression, the square of the l2-norm of the coefficient vector
\thetais assumed to be bounded above by p, where p is the length of\theta\insertCiteKifer2012DPpack. So, domain could be defined asdomain <- list("constraints"=function(coeff) coeff%*%coeff-length(coeff), "jacobian"=function(coeff) 2*coeff).zetaPositive real number denoting the upper bound on the l2-norm value of the gradient of the loss function, as required to ensure differential privacy.
lambdaPositive real number corresponding to the upper bound of the Eigenvalues of the Hessian of the loss function for all possible inputs.
gammaNonnegative real number representing the regularization constant.
mapXy.grOptional function representing the gradient of the map function with respect to the values in
coeff. If given, must be of the formmapXy.gr(X, coeff), whereXis a matrix andcoeffis 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. SeemapXy.gr.linearfor an example. If not given, non-gradient based optimization methods are used to compute the coefficient values in fitting the model.loss.grOptional function representing the gradient of the loss function with respect to
y.hatand of the formloss.gr(y.hat, y), wherey.hatandyare 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. Seeloss.gr.squared.errorfor an example. If not given, non-gradient based optimization methods are used to compute the coefficient values in fitting the model.regularizer.grOptional function representing the gradient of the regularization function with respect to
coeffand of the formregularizer.gr(coeff). Should return a vector. Seeregularizer.gr.l2for an example. Ifregularizeris given as a string, this value is ignored. If not given andregularizeris a function, non-gradient based optimization methods are used to compute the coefficient values in fitting the model.
Returns
A new EmpiricalRiskMinimizationDP.KST object.
Method fit()
Fit the differentially private emprirical risk minimization
model. The function runs the objective perturbation algorithm
\insertCiteKifer2012DPpack 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
nloptr function 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 and the Jacobian of the constraint
function are utilized for the algorithm, and the NLOPT_LD_MMA method is
used. If one or more of these gradient functions are not given, the
NLOPT_LN_COBYLA method is used. The resulting privacy-preserving
coefficients are stored in coeff.
Usage
EmpiricalRiskMinimizationDP.KST$fit( X, y, upper.bounds, lower.bounds, add.bias = FALSE )
Arguments
XDataframe of data to be fit.
yVector or matrix of true values for each row of
X.upper.boundsNumeric vector of length
ncol(X)+1giving upper bounds on the values in each column ofXand the values ofy. The last value in the vector is assumed to be the upper bound ony, while the firstncol(X)values are assumed to be in the same order as the corresponding columns ofX. Any value in the columns ofXand inylarger than the corresponding upper bound is clipped at the bound.lower.boundsNumeric vector of length
ncol(X)+1giving lower bounds on the values in each column ofXand the values ofy. The last value in the vector is assumed to be the lower bound ony, while the firstncol(X)values are assumed to be in the same order as the corresponding columns ofX. Any value in the columns ofXand inylarger than the corresponding lower bound is clipped at the bound.add.biasBoolean indicating whether to add a bias term to
X. Defaults to FALSE.
Method predict()
Predict y values for given X using the fitted coefficients.
Usage
EmpiricalRiskMinimizationDP.KST$predict(X, add.bias = FALSE)
Arguments
XDataframe of data on which to make predictions. Must be of same form as
Xused to fit coefficients.add.biasBoolean indicating whether to add a bias term to
X. Defaults to FALSE. If add.bias was set to TRUE when fitting the coefficients, add.bias should be set to TRUE for predictions.
Returns
Matrix of predicted y values corresponding to each row of X.
Method clone()
The objects of this class are cloneable with this method.
Usage
EmpiricalRiskMinimizationDP.KST$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
References
\insertRefKifer2012DPpack
Examples
# Build example dataset
n <- 500
X <- data.frame(X=seq(-1,1,length.out = n))
true.theta <- c(-.3,.5) # First element is bias term
p <- length(true.theta)
y <- true.theta[1] + as.matrix(X)%*%true.theta[2:p] + stats::rnorm(n=n,sd=.1)
# Construct object for linear regression
mapXy <- function(X, coeff) X%*%coeff
loss <- function(y.hat, y) (y.hat-y)^2/2
regularizer <- 'l2' # Alternatively, function(coeff) coeff%*%coeff/2
eps <- 1
delta <- 1
domain <- list("constraints"=function(coeff) coeff%*%coeff-length(coeff),
"jacobian"=function(coeff) 2*coeff)
# Set p to be the number of predictors desired including intercept term (length of coeff)
zeta <- 2*p^(3/2) # Proper bound for linear regression
lambda <- p # Proper bound for linear regression
gamma <- 1
mapXy.gr <- function(X, coeff) t(X)
loss.gr <- function(y.hat, y) y.hat-y
regularizer.gr <- function(coeff) coeff
ermdp <- EmpiricalRiskMinimizationDP.KST$new(mapXy, loss, 'l2', eps, delta,
domain, zeta, lambda,
gamma, mapXy.gr, loss.gr,
regularizer.gr)
# Fit with data
# We must assume y is a matrix with values between -p and p (-2 and 2
# for this example)
upper.bounds <- c(1, 2) # Bounds for X and y
lower.bounds <- c(-1,-2) # Bounds for X and y
ermdp$fit(X, y, upper.bounds, lower.bounds, add.bias=TRUE)
ermdp$coeff # Gets private coefficients
# Predict new data points
# Build a test dataset
Xtest <- data.frame(X=c(-.5, -.25, .1, .4))
predicted.y <- ermdp$predict(Xtest, add.bias=TRUE)
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