A Model Regime of Privacy Protection (Version 2.0)

This version incorporates and responds to the many comments that we received to Version 1.1, which we released on March 10, 2005. Privacy protection in the United States has often been criticized, but critics have too infrequently suggested specific proposals for reform. Recently, there has been significant legislative interest at both the federal and state levels in addressing the privacy of personal information. This was sparked when ChoicePoint, one of the largest data brokers in the United States with records on almost every adult American citizen, sold data on about 145,000 people to fraudulent businesses set up by identity thieves. Other companies announced security breaches, including LexisNexis, from which personal information about 32,000 people was improperly accessed. Senator Schumer criticized Westlaw for making available to certain subscribers personal information including Social Security Numbers (SSNs). In the aftermath of the ChoicePoint debacle and other major information security breaches, both of us have been asked by Congressional legislative staffers, state legislative policymakers, journalists, academics, and others about what specifically should be done to better regulate information privacy. In response to these questions, we believe that it is imperative to have a discussion of concrete legislative solutions to privacy problems. What appears below is our attempt at such an endeavor. Privacy experts have long suggested that information collection be consistent with Fair Information Practices. This Model Regime incorporates many of those practices and applies them specifically to the context of commercial data brokers such as ChoicePoint. We hope that this will provide useful guidance to legislators and policymakers in crafting laws and regulations. We also intend this to be a work-in-progress in which we collaborate with others. We have welcomed input from other academics, policymakers, journalists, and experts as well as from the industries and businesses that will be subject to the regulations we propose. We have incorporated criticisms and constructive suggestions, and we will continue to update this Model Regime to include the comments we find most helpful and illuminating. Notice, Consent, Control, and Access 1. Universal Notice 2. Meaningful Informed Consent 3. One-Step Exercise of Rights 4. Individual Credit Management 5. Access to and Accuracy of Personal Information Security of Personal Information 6. Secure Identification 7. Disclosure of Security Breaches Business Access to and Use of Personal Information 8. Social Security Number Use Limitation 9. Access and Use Restrictions for Public Records 10. Curbing Excessive Uses of Background Checks 11. Private Investigators Government Access to and Use of Personal Data 12. Limiting Government Access to Business and Financial Records 13. Government Data Mining 14. Control of Government Maintenance of Personal Information Privacy Innovation and Enforcement 15. Preserving the Innovative Role of the States 16. Effective Enforcement of Privacy Rights Commentar

A Model Regime of Privacy Protection (Version 2.0)

This version incorporates and responds to the many comments that we received to Version 1.1, which we released on March 10, 2005. Privacy protection in the United States has often been criticized, but critics have too infrequently suggested specific proposals for reform. Recently, there has been significant legislative interest at both the federal and state levels in addressing the privacy of personal information. This was sparked when ChoicePoint, one of the largest data brokers in the United States with records on almost every adult American citizen, sold data on about 145,000 people to fraudulent businesses set up by identity thieves. Other companies announced security breaches, including LexisNexis, from which personal information about 32,000 people was improperly accessed. Senator Schumer criticized Westlaw for making available to certain subscribers personal information including Social Security Numbers (SSNs). In the aftermath of the ChoicePoint debacle and other major information security breaches, both of us have been asked by Congressional legislative staffers, state legislative policymakers, journalists, academics, and others about what specifically should be done to better regulate information privacy. In response to these questions, we believe that it is imperative to have a discussion of concrete legislative solutions to privacy problems. What appears below is our attempt at such an endeavor. Privacy experts have long suggested that information collection be consistent with Fair Information Practices. This Model Regime incorporates many of those practices and applies them specifically to the context of commercial data brokers such as ChoicePoint. We hope that this will provide useful guidance to legislators and policymakers in crafting laws and regulations. We also intend this to be a work-in-progress in which we collaborate with others. We have welcomed input from other academics, policymakers, journalists, and experts as well as from the industries and businesses that will be subject to the regulations we propose. We have incorporated criticisms and constructive suggestions, and we will continue to update this Model Regime to include the comments we find most helpful and illuminating. Notice, Consent, Control, and Access 1. Universal Notice 2. Meaningful Informed Consent 3. One-Step Exercise of Rights 4. Individual Credit Management 5. Access to and Accuracy of Personal Information Security of Personal Information 6. Secure Identification 7. Disclosure of Security Breaches Business Access to and Use of Personal Information 8. Social Security Number Use Limitation 9. Access and Use Restrictions for Public Records 10. Curbing Excessive Uses of Background Checks 11. Private Investigators Government Access to and Use of Personal Data 12. Limiting Government Access to Business and Financial Records 13. Government Data Mining 14. Control of Government Maintenance of Personal Information Privacy Innovation and Enforcement 15. Preserving the Innovative Role of the States 16. Effective Enforcement of Privacy Rights Commentar

A Framework for High-Accuracy Privacy-Preserving Mining

To preserve client privacy in the data mining process, a variety of techniques based on random perturbation of data records have been proposed recently. In this paper, we present a generalized matrix-theoretic model of random perturbation, which facilitates a systematic approach to the design of perturbation mechanisms for privacy-preserving mining. Specifically, we demonstrate that (a) the prior techniques differ only in their settings for the model parameters, and (b) through appropriate choice of parameter settings, we can derive new perturbation techniques that provide highly accurate mining results even under strict privacy guarantees. We also propose a novel perturbation mechanism wherein the model parameters are themselves characterized as random variables, and demonstrate that this feature provides significant improvements in privacy at a very marginal cost in accuracy. While our model is valid for random-perturbation-based privacy-preserving mining in general, we specifically evaluate its utility here with regard to frequent-itemset mining on a variety of real datasets. The experimental results indicate that our mechanisms incur substantially lower identity and support errors as compared to the prior techniques

Efficient Privacy Preserving Distributed Clustering Based on Secret Sharing

In this paper, we propose a privacy preserving distributed clustering protocol for horizontally partitioned data based on a very efficient homomorphic additive secret sharing scheme. The model we use for the protocol is novel in the sense that it utilizes two non-colluding third parties. We provide a brief security analysis of our protocol from information theoretic point of view, which is a stronger security model. We show communication and computation complexity analysis of our protocol along with another protocol previously proposed for the same problem. We also include experimental results for computation and communication overhead of these two protocols. Our protocol not only outperforms the others in execution time and communication overhead on data holders, but also uses a more efficient model for many data mining applications

PriPeARL: A Framework for Privacy-Preserving Analytics and Reporting at LinkedIn

Preserving privacy of users is a key requirement of web-scale analytics and reporting applications, and has witnessed a renewed focus in light of recent data breaches and new regulations such as GDPR. We focus on the problem of computing robust, reliable analytics in a privacy-preserving manner, while satisfying product requirements. We present PriPeARL, a framework for privacy-preserving analytics and reporting, inspired by differential privacy. We describe the overall design and architecture, and the key modeling components, focusing on the unique challenges associated with privacy, coverage, utility, and consistency. We perform an experimental study in the context of ads analytics and reporting at LinkedIn, thereby demonstrating the tradeoffs between privacy and utility needs, and the applicability of privacy-preserving mechanisms to real-world data. We also highlight the lessons learned from the production deployment of our system at LinkedIn.Comment: Conference information: ACM International Conference on Information and Knowledge Management (CIKM 2018