A method for privatizing an iteratively reweighted least squares (IRLS) solution includes perturbing a first moment of a dataset by adding noise and perturbing a second moment of the dataset by adding noise. The method also includes obtaining the IRLS solution based on the perturbed first moment and the perturbed second moment. The method further includes generating a differentially private output based on the IRLS solution.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for privatizing an iteratively reweighted least squares (IRLS) solution, comprising: perturbing a first moment of a dataset by adding noise; perturbing a second moment of the dataset by adding noise; obtaining the IRLS solution based on the perturbed first moment and the perturbed second moment; and generating a differentially private output based on the IRLS solution. 2. The method of claim 1, in which the first moment is a mean of the dataset and the second moment is a covariance of the dataset. 3. The method of claim 1, further comprising: perturbing the first moment via a Laplace mechanism; and perturbing the second moment by adding Wishart noise. 4. The method of claim 1, further comprising determining an amount of noise to add to the first moment via a Laplace mechanism based on an amount of change that can occur to a data point of the dataset. 5. The method of claim 1, in which the IRLS solution is a differential private (DP) iteratively reweighted least squares function. 6. The method of claim 1, in which the IRLS solution is a concentrated differential private (CDP) iteratively reweighted least squares function. 7. An apparatus for privatizing an iteratively reweighted least squares (IRLS) solution, the apparatus comprising: a memory; and at least one processor coupled to the memory, the at least one processor configured: to perturb a first moment of a dataset by adding noise; to perturb a second moment of the dataset by adding noise; to obtain the IRLS solution based on the perturbed first moment and the perturbed second moment; and to generate a differentially private output based on the IRLS solution. 8. The apparatus of claim 7, in which the first moment is a mean of the dataset and the second moment is a covariance of the dataset. 9. The apparatus of claim 7, in which the at least one processor is further configured: to perturb the first moment via a Laplace mechanism; and to perturb the second moment by adding Wishart noise. 10. The apparatus of claim 7, in which the at least one processor is further configured to determine an amount of noise to add to the first moment via a Laplace mechanism based on an amount of change that can occur to a data point of the dataset. 11. The apparatus of claim 7, in which the IRLS solution is a differential private (DP) iteratively reweighted least squares function. 12. The apparatus of claim 7, in which the IRLS solution is a concentrated differential private (CDP) iteratively reweighted least squares function. 13. An apparatus for privatizing an iteratively reweighted least squares (IRLS) solution, comprising: means for perturbing a first moment of a dataset by adding noise; means for perturbing a second moment of the dataset by adding noise; and means for obtaining the IRLS solution based on the perturbed first moment and the perturbed second moment; and means for generating a differentially private output based on the IRLS solution. 14. The apparatus of claim 13, in which the first moment is a mean of the dataset and the second moment is a covariance of the dataset. 15. The apparatus of claim 13, further comprising: means for perturbing the first moment via a Laplace mechanism; and means for perturbing the second moment by adding Wishart noise. 16. The apparatus of claim 13, further comprising means for determining an amount of noise to add to the first moment via a Laplace mechanism based on an amount of change that can occur to a data point of the dataset. 17. The apparatus of claim 13, in which the IRLS solution is a differential private (DP) iteratively reweighted least squares function. 18. The apparatus of claim 13, in which the IRLS solution is a concentrated differential private (CDP) iteratively reweighted least squares function. 19. A non-transitory computer-readable medium having program code recorded thereon for privatizing an iteratively reweighted least squares (IRLS) solution, the program code executed by a processor and comprising: program code to perturb a first moment of a dataset by adding noise; program code to perturb a second moment of the dataset by adding noise; program code to obtain the IRLS solution based on the perturbed first moment and the perturbed second moment; and program code to generate a differentially private output based on the IRLS solution. 20. The non-transitory computer-readable medium of claim 19, in which the first moment is a mean of the dataset and the second moment is a covariance of the dataset. 21. The non-transitory computer-readable medium of claim 19, further comprising: program code to perturb the first moment via a Laplace mechanism; and program code to perturb the second moment by adding Wishart noise. 22. The non-transitory computer-readable medium of claim 19, in which the program code to perturb the first moment further comprises program code to determine an amount of noise to add to the first moment via a Laplace mechanism based on an amount of change that can occur to a data point of the dataset. 23. The non-transitory computer-readable medium of claim 19, in which the IRLS solution is a differential private (DP) iteratively reweighted least squares function. 24. The non-transitory computer-readable medium of claim 19, in which the IRLS solution is a concentrated differential private (CDP) iteratively reweighted least squares function.