William H. Sorrell, Attorney General of Vermont, et al. v. IMS Health Inc., et al. - Amicus Brief in Support of Petitioners

On April 26, 2011, the US Supreme Court will hear oral arguments in the Vermont data mining case, Sorrell v. IMS Health Inc. Respondents claim this is the most important commercial speech case in a decade. Petitioner (the State of Vermont) argues this is the most important medical privacy case since Whalen v. Roe. The is an amicus brief supporting Vermont, written by law professors and submitted on behalf of the New England Journal of Medicin

Hacking Smart Machines with Smarter Ones: How to Extract Meaningful Data from Machine Learning Classifiers

Machine Learning (ML) algorithms are used to train computers to perform a variety of complex tasks and improve with experience. Computers learn how to recognize patterns, make unintended decisions, or react to a dynamic environment. Certain trained machines may be more effective than others because they are based on more suitable ML algorithms or because they were trained through superior training sets. Although ML algorithms are known and publicly released, training sets may not be reasonably ascertainable and, indeed, may be guarded as trade secrets. While much research has been performed about the privacy of the elements of training sets, in this paper we focus our attention on ML classifiers and on the statistical information that can be unconsciously or maliciously revealed from them. We show that it is possible to infer unexpected but useful information from ML classifiers. In particular, we build a novel meta-classifier and train it to hack other classifiers, obtaining meaningful information about their training sets. This kind of information leakage can be exploited, for example, by a vendor to build more effective classifiers or to simply acquire trade secrets from a competitor's apparatus, potentially violating its intellectual property rights

Towards trajectory anonymization: a generalization-based approach

Trajectory datasets are becoming popular due to the massive usage of GPS and locationbased services. In this paper, we address privacy issues regarding the identification of individuals in static trajectory datasets. We first adopt the notion of k-anonymity to trajectories and propose a novel generalization-based approach for anonymization of trajectories. We further show that releasing anonymized trajectories may still have some privacy leaks. Therefore we propose a randomization based reconstruction algorithm for releasing anonymized trajectory data and also present how the underlying techniques can be adapted to other anonymity standards. The experimental results on real and synthetic trajectory datasets show the effectiveness of the proposed techniques

Mining whole sample mass spectrometry proteomics data for biomarkers: an overview

In this paper we aim to provide a concise overview of designing and conducting an MS proteomics experiment in such a way as to allow statistical analysis that may lead to the discovery of novel biomarkers. We provide a summary of the various stages that make up such an experiment, highlighting the need for experimental goals to be decided upon in advance. We discuss issues in experimental design at the sample collection stage, and good practise for standardising protocols within the proteomics laboratory. We then describe approaches to the data mining stage of the experiment, including the processing steps that transform a raw mass spectrum into a useable form. We propose a permutation-based procedure for determining the significance of reported error rates. Finally, because of its general advantages in speed and cost, we suggest that MS proteomics may be a good candidate for an early primary screening approach to disease diagnosis, identifying areas of risk and making referrals for more specific tests without necessarily making a diagnosis in its own right. Our discussion is illustrated with examples drawn from experiments on bovine blood serum conducted in the Centre for Proteomic Research (CPR) at Southampton University

Illinois Water Quality and the Clean Water Act

This report contains the conclusions of a study performed by the Environmental Law and Policy Center of the Midwest ("ELPC") on the water quality of Illinois' rivers, lakes and streams, and Illinois' implementation of the Clean Water Act. The Lumpkin Foundation of Mattoon, Illinois provided funding for the study. Because the Illinois Environmental Protection Agency ("IEPA") is responsible for implementing the Clean Water Act in Illinois and for preparing most of the key reports relating to Illinois water quality, our research necessarily focused on the work of that agency. ELPC studied the publicly available IEPA data on a number of key indicators of water quality and the strength of a number of elements of IEPA's water pollution control efforts. Within the resources available for this study, ELPC also looked, for comparison purposes, at data from federal agencies and selected data collected by pollution control agencies of other states. Further, ELPC conducted interviews with federal and state officials and others with knowledge relating to the relative strengths and weaknesses of the Illinois water quality program. The study considered the following areas: Illinois Water Quality; Amount and Kind of Water Quality Data Collected; Strength of Water Quality Standards; Adequacy of Permit Conditions For Preventing Violations of Water Quality Standards; Permit Enforcement; Illinois' Stormwater and Combined Sewer Overflow Programs; Illinois' Non-Point Source Programs. Project Team: Howard Learner, Executive Director; Ann Alexander, Staff Attorney; Faith Bugel, Staff Attorney; Albert Ettinger, Senior Staff Attorney*; Shannon Fisk, Staff Attorney. * Principal Autho

Routes for breaching and protecting genetic privacy

We are entering the era of ubiquitous genetic information for research, clinical care, and personal curiosity. Sharing these datasets is vital for rapid progress in understanding the genetic basis of human diseases. However, one growing concern is the ability to protect the genetic privacy of the data originators. Here, we technically map threats to genetic privacy and discuss potential mitigation strategies for privacy-preserving dissemination of genetic data.Comment: Draft for comment

iPDA: An Integrity-Protecting Private Data Aggregation Scheme for Wireless Sensor Networks

Data aggregation is an efficient mechanism widely used in wireless sensor networks (WSN) to collect statistics about data of interests. However, the shared-medium nature of communication makes the WSNs are vulnerable to eavesdropping and packet tampering/injection by adversaries. Hence, how to protect data privacy and data integrity are two major challenges for data aggregation in wireless sensor networks. In this paper, we present iPDA??????an integrity-protecting private data aggregation scheme. In iPDA, data privacy is achieved through data slicing and assembling technique; and data integrity is achieved through redundancy by constructing disjoint aggregation paths/trees to collect data of interests. In iPDA, the data integrity-protection and data privacy-preservation mechanisms work synergistically. We evaluate the iPDA scheme in terms of the efficacy of privacy preservation, communication overhead, and data aggregation accuracy, comparing with a typical data aggregation scheme--- TAG, where no integrity protection and privacy preservation is provided. Both theoretical analysis and simulation results show that iPDA achieves the design goals while still maintains the efficiency of data aggregation