Software-implemented attack tolerance for critical information retrieval

The fast-growing reliance of our daily life upon online information services often demands an appropriate level of privacy protection as well as highly available service provision. However, most existing solutions have attempted to address these problems separately. This thesis investigates and presents a solution that provides both privacy protection and fault tolerance for online information retrieval. A new approach to Attack-Tolerant Information Retrieval (ATIR) is developed based on an extension of existing theoretical results for Private Information Retrieval (PIR). ATIR uses replicated services to protect a user's privacy and to ensure service availability. In particular, ATIR can tolerate any collusion of up to t servers for privacy violation and up to ƒ faulty (either crashed or malicious) servers in a system with k replicated servers, provided that k ≥ t + ƒ + 1 where t ≥ 1 and ƒ ≤ t. In contrast to other related approaches, ATIR relies on neither enforced trust assumptions, such as the use of tanker-resistant hardware and trusted third parties, nor an increased number of replicated servers. While the best solution known so far requires k (≥ 3t + 1) replicated servers to cope with t malicious servers and any collusion of up to t servers with an O(n^*^) communication complexity, ATIR uses fewer servers with a much improved communication cost, O(n1/2)(where n is the size of a database managed by a server).The majority of current PIR research resides on a theoretical level. This thesis provides both theoretical schemes and their practical implementations with good performance results. In a LAN environment, it takes well under half a second to use an ATIR service for calculations over data sets with a size of up to 1MB. The performance of the ATIR systems remains at the same level even in the presence of server crashes and malicious attacks. Both analytical results and experimental evaluation show that ATIR offers an attractive and practical solution for ever-increasing online information applications

Bayesian empirical likelihood for quantile regression

Bayesian inference provides a flexible way of combining data with prior information. However, quantile regression is not equipped with a parametric likelihood, and therefore, Bayesian inference for quantile regression demands careful investigation. This paper considers the Bayesian empirical likelihood approach to quantile regression. Taking the empirical likelihood into a Bayesian framework, we show that the resultant posterior from any fixed prior is asymptotically normal; its mean shrinks toward the true parameter values, and its variance approaches that of the maximum empirical likelihood estimator. A more interesting case can be made for the Bayesian empirical likelihood when informative priors are used to explore commonality across quantiles. Regression quantiles that are computed separately at each percentile level tend to be highly variable in the data sparse areas (e.g., high or low percentile levels). Through empirical likelihood, the proposed method enables us to explore various forms of commonality across quantiles for efficiency gains. By using an MCMC algorithm in the computation, we avoid the daunting task of directly maximizing empirical likelihood. The finite sample performance of the proposed method is investigated empirically, where substantial efficiency gains are demonstrated with informative priors on common features across several percentile levels. A theoretical framework of shrinking priors is used in the paper to better understand the power of the proposed method.Comment: Published in at http://dx.doi.org/10.1214/12-AOS1005 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Rejoinder

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134939/1/insr12181.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/134939/2/insr12181_am.pd

Posterior Inference in Bayesian Quantile Regression with Asymmetric Laplace Likelihood

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135059/1/insr12114.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/135059/2/insr12114_am.pd

An Uncertain QFD Approach for the Strategic Management of Logistics Services

Due to customers’ growing concern about logistics performances related to products, logistics service increasingly contributes to the core competence of an enterprise or product, which calls an appropriate tool to develop effective strategic actions to improve logistics performances and gain customer satisfaction. Therefore, an uncertain quality function deployment (QFD) approach for selecting the most effective strategic actions in terms of efficiency to meet the customer requirements is developed in this paper, which integrates uncertainty theory into the traditional QFD methodology in order to rationally deal with imprecise information inherently involved in the QFD process. The framework and systematic procedures of the approach are presented in the context of logistics services. Specifically, the calculations for the prioritization of strategic actions are discussed in detail, in which uncertain variables are used to capture the linguistic judgements given by customers and experts. Applications of the proposed approach are presented as well for illustration

Roles of DNA damage in renal tubular epithelial cells injury

The prevalence of renal diseases including acute kidney injury (AKI) and chronic kidney disease (CKD) is increasing worldwide. However, the pathogenesis of most renal diseases is still unclear and effective treatments are still lacking. DNA damage and the related DNA damage response (DDR) have been confirmed as common pathogenesis of acute kidney injury and chronic kidney disease. Reactive oxygen species (ROS) induced DNA damage is one of the most common types of DNA damage involved in the pathogenesis of acute kidney injury and chronic kidney disease. In recent years, several developments have been made in the field of DNA damage. Herein, we review the roles and developments of DNA damage and DNA damage response in renal tubular epithelial cell injury in acute kidney injury and chronic kidney disease. In this review, we conclude that focusing on DNA damage and DNA damage response may provide valuable diagnostic biomarkers and treatment strategies for renal diseases including acute kidney injury and chronic kidney disease

Differentially Private SGDA for Minimax Problems

Stochastic gradient descent ascent (SGDA) and its variants have been the workhorse for solving minimax problems. However, in contrast to the well-studied stochastic gradient descent (SGD) with differential privacy (DP) constraints, there is little work on understanding the generalization (utility) of SGDA with DP constraints. In this paper, we use the algorithmic stability approach to establish the generalization (utility) of DP-SGDA in different settings. In particular, for the convex-concave setting, we prove that the DP-SGDA can achieve an optimal utility rate in terms of the weak primal-dual population risk in both smooth and non-smooth cases. To our best knowledge, this is the first-ever-known result for DP-SGDA in the non-smooth case. We further provide its utility analysis in the nonconvex-strongly-concave setting which is the first-ever-known result in terms of the primal population risk. The convergence and generalization results for this nonconvex setting are new even in the non-private setting. Finally, numerical experiments are conducted to demonstrate the effectiveness of DP-SGDA for both convex and nonconvex cases

An Uncertain QFD Approach for the Strategic Management of Logistics Services

Due to customers' growing concern about logistics performances related to products, logistics service increasingly contributes to the core competence of an enterprise or product, which calls an appropriate tool to develop effective strategic actions to improve logistics performances and gain customer satisfaction. Therefore, an uncertain quality function deployment (QFD) approach for selecting the most effective strategic actions in terms of efficiency to meet the customer requirements is developed in this paper, which integrates uncertainty theory into the traditional QFD methodology in order to rationally deal with imprecise information inherently involved in the QFD process. The framework and systematic procedures of the approach are presented in the context of logistics services. Specifically, the calculations for the prioritization of strategic actions are discussed in detail, in which uncertain variables are used to capture the linguistic judgements given by customers and experts. Applications of the proposed approach are presented as well for illustration