Heavy Hitters and the Structure of Local Privacy

We present a new locally differentially private algorithm for the heavy hitters problem which achieves optimal worst-case error as a function of all standardly considered parameters. Prior work obtained error rates which depend optimally on the number of users, the size of the domain, and the privacy parameter, but depend sub-optimally on the failure probability. We strengthen existing lower bounds on the error to incorporate the failure probability, and show that our new upper bound is tight with respect to this parameter as well. Our lower bound is based on a new understanding of the structure of locally private protocols. We further develop these ideas to obtain the following general results beyond heavy hitters. $\bullet$ Advanced Grouposition: In the local model, group privacy for $k$ users degrades proportionally to $\approx \sqrt{k}$, instead of linearly in $k$ as in the central model. Stronger group privacy yields improved max-information guarantees, as well as stronger lower bounds (via "packing arguments"), over the central model. $\bullet$ Building on a transformation of Bassily and Smith (STOC 2015), we give a generic transformation from any non-interactive approximate-private local protocol into a pure-private local protocol. Again in contrast with the central model, this shows that we cannot obtain more accurate algorithms by moving from pure to approximate local privacy

System and method to assess signal similarity with applications to diagnostics and prognostics

Signal processing technology for assessing dynamic system similarity for fault detection and other applications is based on time- and frequency-domain time series analysis techniques and compares the entire autocorrelation structure of a test and reference signal series. The test and reference signals are first subjected to similar pre-processing to help guarantee signal stationarity. Pre-processing may include formation of multivariate signal clusters, filtering and sampling. Multivariate periodograms or autocovariance functions are then calculated for each signal series. Test statistics are computed and assessed to determine the equality of the test and reference signals. When the difference between sample autocovariance functions or periodograms of such signals exceeds a preselected threshold value, fault detection signals and/or related diagnostic information are provided as output to a user

Marginal Release Under Local Differential Privacy

Many analysis and machine learning tasks require the availability of marginal statistics on multidimensional datasets while providing strong privacy guarantees for the data subjects. Applications for these statistics range from finding correlations in the data to fitting sophisticated prediction models. In this paper, we provide a set of algorithms for materializing marginal statistics under the strong model of local differential privacy. We prove the first tight theoretical bounds on the accuracy of marginals compiled under each approach, perform empirical evaluation to confirm these bounds, and evaluate them for tasks such as modeling and correlation testing. Our results show that releasing information based on (local) Fourier transformations of the input is preferable to alternatives based directly on (local) marginals

Private Multiplicative Weights Beyond Linear Queries

A wide variety of fundamental data analyses in machine learning, such as linear and logistic regression, require minimizing a convex function defined by the data. Since the data may contain sensitive information about individuals, and these analyses can leak that sensitive information, it is important to be able to solve convex minimization in a privacy-preserving way. A series of recent results show how to accurately solve a single convex minimization problem in a differentially private manner. However, the same data is often analyzed repeatedly, and little is known about solving multiple convex minimization problems with differential privacy. For simpler data analyses, such as linear queries, there are remarkable differentially private algorithms such as the private multiplicative weights mechanism (Hardt and Rothblum, FOCS 2010) that accurately answer exponentially many distinct queries. In this work, we extend these results to the case of convex minimization and show how to give accurate and differentially private solutions to *exponentially many* convex minimization problems on a sensitive dataset

A physical layer network coding based modify-and-forward with opportunistic secure cooperative transmission protocol

This paper investigates a new secure relaying scheme, namely physical layer network coding based modify-and-forward (PMF), in which a relay node linearly combines the decoded data sent by a source node with an encrypted key before conveying the mixed data to a destination node. We first derive the general expression for the generalized secrecy outage probability (GSOP) of the PMF scheme and then use it to analyse the GSOP performance of various relaying and direct transmission strategies. The GSOP performance comparison indicates that these transmission strategies offer different advantages depending on the channel conditions and target secrecy rates, and relaying is not always desirable in terms of secrecy. Subsequently, we develop an opportunistic secure transmission protocol for cooperative wireless relay networks and formulate an optimisation problem to determine secrecy rate thresholds (SRTs) to dynamically select the optimal transmission strategy for achieving the lowest GSOP. The conditions for the existence of the SRTs are derived for various channel scenarios

Idiopathic interstitial pneumonia: Do community and academic physicians agree on diagnosis?

Rationale: Treatment and prognoses of diffuse parenchymal lung diseases (DPLDs) varies by diagnosis. Obtaining a uniform diagnosis among observers is difficult. Objectives: Evaluate diagnostic agreement between academic and community-based physicians for patients with DPLDs, and determine if an interactive approach between clinicians, radiologists, and pathologists improved diagnostic agreement in community and academic centers. Methods: Retrospective review of 39 patients with DPLD. A total of 19 participants reviewed cases at 2 community locations and 1 academic location. Information from the history, physical examination, pulmonary function testing, high-resolution computed tomography, and surgical lung biopsy was collected. Data were presented in the same sequential fashion to three groups of physicians on separate days. Measurements and Main Results: Each observer’s diagnosis was coded into one of eight categories. A statistic allowing formultiple raters was used to assess agreement in diagnosis. Interactions between clinicians, radiologists, and pathologists improved interobserver agreement at both community and academic sites; however, final agreement was better within academic centers (Kappa= 0.55–0.71) than within community centers (Kappa=0.32–0.44). Clinically significant disagreement was present between academic and communitybased physicians (Kappa=0.11–0.56). Community physicians were more likely to assign a final diagnosis of idiopathic pulmonary fibrosis compared with academic physicians. Conclusions: Significant disagreement exists in the diagnosis of DPLD between physicians based in communities compared with those in academic centers. Wherever possible, patients should be referred to centers with expertise in diffuse parenchymal lung disorders to help clarify the diagnosis and provide suggestions regarding treatment options.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/91941/1/2007 AJRCCM Idiopathic interstitial pneumonia - Do community and academic physicians agree on diagnosis.pd

Gene expression profiling of noninvasive primary urothelial tumours using microarrays

At present, the mechanism leading to bladder cancer is still poorly understood, and our knowledge about early events in tumorigenesis is limited. This study describes the changes in gene expression occurring during the neoplastic transition from normal bladder urothelium to primary Ta tumours. Using DNA microarrays, we identified novel differentially expressed genes in Ta tumours compared to normal bladder, and genes that were altered in high-grade tumours. Among the mostly changed genes between normal bladder and Ta tumours, we found genes related to the cytoskeleton (keratin 7 and syndecan 1), and transcription (high mobility group AT-hook 1). Altered genes in high-grade tumours were related to cell cycle (cyclin-dependent kinase 4) and transcription (jun d proto-oncogene). Furthermore, we showed the presence of high keratin 7 transcript expression in bladder cancer, and Western blotting analysis revealed three major molecular isoforms of keratin 7 in the tissues. These could be detected in urine sediments from bladder tumour patients