Using Participants' Utility Functions to Compare Versions of Differential Privacy

We use decision theory to compare variants of differential privacy from the perspective of prospective study participants. We posit the existence of a preference ordering on the set of potential consequences that study participants can incur, which enables the analysis of individual utility functions. Drawing upon the theory of measurement, we argue that changes in expected utilities should be measured via the classic Euclidean metric. We then consider the question of which privacy guarantees would be more appealing for individuals under different decision settings. Through our analysis, we found that the nature of the potential participant's utility function, along with the specific values of $\epsilon$ and $\delta$, can greatly alter which privacy guarantees are preferable

This Thing Called Fairness: Disciplinary Confusion Realizing a Value in Technology

The explosion in the use of software in important sociotechnical systems has renewed focus on the study of the way technical constructs reflect policies, norms, and human values. This effort requires the engagement of scholars and practitioners from many disciplines. And yet, these disciplines often conceptualize the operative values very differently while referring to them using the same vocabulary. The resulting conflation of ideas confuses discussions about values in technology at disciplinary boundaries. In the service of improving this situation, this paper examines the value of shared vocabularies, analytics, and other tools that facilitate conversations about values in light of these disciplinary specific conceptualizations, the role such tools play in furthering research and practice, outlines different conceptions of ``fairness''deployed in discussions about computer systems, and provides an analytic tool for interdisciplinary discussions and collaborations around the concept of fairness. We use a case study of risk assessments in criminal justice applications to both motivate our effort--describing how conflation of different concepts under the banner of ``fairness'' led to unproductive confusion--and illustrate the value of the fairness analytic by demonstrating how the rigorous analysis it enables can assist in identifying key areas of theoretical, political, and practical misunderstanding or disagreement, and where desired support alignment or collaboration in the absence of consensus

Magneto-optical Kerr Effect Studies of Square Artificial Spin Ice

We report a magneto-optical Kerr effect study of the collective magnetic response of artificial square spin ice, a lithographically-defined array of single-domain ferromagnetic islands. We find that the anisotropic inter-island interactions lead to a non-monotonic angular dependence of the array coercive field. Comparisons with micromagnetic simulations indicate that the two perpendicular sublattices exhibit distinct responses to island edge roughness, which clearly influence the magnetization reversal process. Furthermore, such comparisons demonstrate that disorder associated with roughness in the island edges plays a hitherto unrecognized but essential role in the collective behavior of these systems.Comment: Physical Review B, Rapid Communications (in press

Big Data Privacy in Emerging Market Fintech and Financial Services: A Research Agenda

The data revolution in low- and middle-income countries is quickly transforming how companies approach emerging markets. As mobile phones and mobile money proliferate, they generate new streams of data that enable innovation in consumer finance, credit, and insurance. Already, this new generation of products are being used by hundreds of millions of consumers, often to use financial services for the first time. However, the collection, analysis, and use of these data, particularly from economically disadvantaged populations, raises serious privacy concerns. This white paper describes a research agenda to advance our understanding of the problem and solution space of data privacy in emerging market fintech and financial services. We highlight five priority areas for research: conducting comprehensive landscape analyses; understanding local definitions of “data privacy”; doc-umenting key sources of risk, and potential technical solutions (such as differential privacy and homomorphic encryption); improving non-technical approaches to data privacy (such as policies and practices); and understanding the tradeoffs involved in deploying privacy-enhancing solutions. Taken together, we hope this research agenda will focus attention on the multi-faceted nature of privacy in emerging markets, and catalyze efforts to develop responsible and consumer-oriented approaches to data-intensive applications

Differential Privacy in Practice: Expose your Epsilons!

Differential privacy is at a turning point. Implementations have been successfully leveraged in private industry, the public sector, and academia in a wide variety of applications, allowing scientists, engineers, and researchers the ability to learn about populations of interest without specifically learning about these individuals. Because differential privacy allows us to quantify cumulative privacy loss, these differentially private systems will, for the first time, allow us to measure and compare the total privacy loss due to these personal data-intensive activities. Appropriately leveraged, this could be a watershed moment for privacy. Like other technologies and techniques that allow for a range of instantiations, implementation details matter. When meaningfully implemented, differential privacy supports deep data-driven insights with minimal worst-case privacy loss. When not meaningfully implemented, differential privacy delivers privacy mostly in name. Using differential privacy to maximize learning while providing a meaningful degree of privacy requires judicious choices with respect to the privacy parameter epsilon, among other factors. However, there is little understanding of what is the optimal value of epsilon for a given system or classes of systems/purposes/data etc. or how to go about figuring it out. To understand current differential privacy implementations and how organizations make these key choices in practice, we conducted interviews with practitioners to learn from their experiences of implementing differential privacy. We found no clear consensus on how to choose epsilon, nor is there agreement on how to approach this and other key implementation decisions. Given the importance of these implementation details there is a need for shared learning amongst the differential privacy community. To serve these purposes, we propose the creation of the Epsilon Registry—a publicly available communal body of knowledge about differential privacy implementations that can be used by various stakeholders to drive the identification and adoption of judicious differentially private implementations

Privacy Guarantees for Personal Mobility Data in Humanitarian Response

Personal mobility data from mobile phones and other sensors are increasingly used to inform policymaking during pandemics,natural disasters, and other humanitarian crises. However, even aggregated mobility traces can reveal private informationabout individual movements to potentially malicious actors. This paper develops and tests an approach for releasing privatemobility data, which provides formal guarantees over the privacy of the underlying subjects. Specifically, we (1) introduce an algorithm for constructing differentially private mobility matrices, and derive privacy and accuracy bounds on this algorithm; (2) use real-world data from mobile phone operators in Afghanistan and Rwanda to show how this algorithm can enable the use of private mobility data in two high-stakes policy decisions: pandemic response and the distribution of humanitarian aid; and (3) discuss practical decisions that need to be made when implementing this approach, such as how to optimally balance privacy and accuracy. Taken together, these results can help enable the responsible use of private mobility data in humanitarian response

Neuropsychological assessment in obsessive-compulsive disorder

Background: Neuropsychological deficits in obsessive-compulsive disorder (OCD) have been encouraged by brain imaging studies suggesting a putative fron to- striatial biological basis of the condition. Studies of neuropsychological functions in OCD have documented deficits in several cognitive domains, particularly with regard to visuospatial abilities, executive functioning, motor speed and memory. The Aim of the present study was to assess neuropsychological profile of patients with OCD. Objectives of the study were to assess and compare the neuropsychological profile of patients with OCD and matched healthy controls. Materials and Methods: Twenty clinically stable outpatients with ICD-10 diagnosis of OCD and equal number of normal controls matched for age, education, gender and handedness were studied using a battery of neuropsychological tests. The tests consisted of verbal and performance tests of intelligence, memory, perceptual motor functions, set test and Wisconsin Card Sorting Test (WCST). Results: On perceptual-motor functions, verbal fluency, executive functions (WCST), intelligence and memory patients with OCD did not show impairments comparable to healthy controls. An attempt to correlate the test findings with the duration of illness, stability of illness and the average drug dose was made and it was found that there was no correlation between the two. Conclusion: The present study does not provide evidence for a localized neuropsychological/cognitive impairment in OCD in cases that are stable for at least three months. Absence of impairments in perceptual-motor functions, verbal fluency, executive functions (WCST), intelligence, and memory does not agree with the results of other studies using these tests