557 research outputs found
Batching of Tasks by Users of Pseudonymous Forums: Anonymity Compromise and Protection
There are a number of forums where people participate under pseudonyms. One
example is peer review, where the identity of reviewers for any paper is
confidential. When participating in these forums, people frequently engage in
"batching": executing multiple related tasks (e.g., commenting on multiple
papers) at nearly the same time. Our empirical analysis shows that batching is
common in two applications we consider \unicode{x2013} peer review and
Wikipedia edits. In this paper, we identify and address the risk of
deanonymization arising from linking batched tasks. To protect against linkage
attacks, we take the approach of adding delay to the posting time of batched
tasks. We first show that under some natural assumptions, no delay mechanism
can provide a meaningful differential privacy guarantee. We therefore propose a
"one-sided" formulation of differential privacy for protecting against linkage
attacks. We design a mechanism that adds zero-inflated uniform delay to events
and show it can preserve privacy. We prove that this noise distribution is in
fact optimal in minimizing expected delay among mechanisms adding independent
noise to each event, thereby establishing the Pareto frontier of the trade-off
between the expected delay for batched and unbatched events. Finally, we
conduct a series of experiments on Wikipedia and Bitcoin data that corroborate
the practical utility of our algorithm in obfuscating batching without
introducing onerous delay to a system
Scheduling for a Processor Sharing System with Linear Slowdown
We consider the problem of scheduling arrivals to a congestion system with a
finite number of users having identical deterministic demand sizes. The
congestion is of the processor sharing type in the sense that all users in the
system at any given time are served simultaneously. However, in contrast to
classical processor sharing congestion models, the processing slowdown is
proportional to the number of users in the system at any time. That is, the
rate of service experienced by all users is linearly decreasing with the number
of users. For each user there is an ideal departure time (due date). A
centralized scheduling goal is then to select arrival times so as to minimize
the total penalty due to deviations from ideal times weighted with sojourn
times. Each deviation is assumed quadratic, or more generally convex. But due
to the dynamics of the system, the scheduling objective function is non-convex.
Specifically, the system objective function is a non-smooth piecewise convex
function. Nevertheless, we are able to leverage the structure of the problem to
derive an algorithm that finds the global optimum in a (large but) finite
number of steps, each involving the solution of a constrained convex program.
Further, we put forward several heuristics. The first is the traversal of
neighbouring constrained convex programming problems, that is guaranteed to
reach a local minimum of the centralized problem. This is a form of a "local
search", where we use the problem structure in a novel manner. The second is a
one-coordinate "global search", used in coordinate pivot iteration. We then
merge these two heuristics into a unified "local-global" heuristic, and
numerically illustrate the effectiveness of this heuristic
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Seeing through black boxes: Tracking transactions through queues under monitoring resource constraints
The problem of optimal allocation of monitoring resources for tracking transactions progressing through a distributed system, modeled as a queueing network, is considered. Two forms of monitoring information are considered, viz., locally unique transaction identifiers, and arrival and departure timestamps of transactions at each processing queue. The timestamps are assumed to be available at all the queues but in the absence of identifiers, only enable imprecise tracking since parallel processing can result in out-of-order departures. On the other hand, identifiers enable precise tracking but are not available without proper instrumentation. Given an instrumentation budget, only a subset of queues can be selected for the production of identifiers, while the remaining queues have to resort to imprecise tracking using timestamps. The goal is then to optimally allocate the instrumentation budget to maximize the overall tracking accuracy. The challenge is that the optimal allocation strategy depends on accuracies of timestamp-based tracking at different queues, which has complex dependencies on the arrival and service processes, and the queueing discipline. We propose two simple heuristics for allocation by predicting the order of timestamp-based tracking accuracies of different queues. We derive sufficient conditions for these heuristics to achieve optimality through the notion of the stochastic comparison of queues. Simulations show that our heuristics are close to optimality, even when the parameters deviate from these conditions
Data-Driven Robust Optimization in Healthcare Applications
abstract: Healthcare operations have enjoyed reduced costs, improved patient safety, and
innovation in healthcare policy over a huge variety of applications by tackling prob-
lems via the creation and optimization of descriptive mathematical models to guide
decision-making. Despite these accomplishments, models are stylized representations
of real-world applications, reliant on accurate estimations from historical data to jus-
tify their underlying assumptions. To protect against unreliable estimations which
can adversely affect the decisions generated from applications dependent on fully-
realized models, techniques that are robust against misspecications are utilized while
still making use of incoming data for learning. Hence, new robust techniques are ap-
plied that (1) allow for the decision-maker to express a spectrum of pessimism against
model uncertainties while (2) still utilizing incoming data for learning. Two main ap-
plications are investigated with respect to these goals, the first being a percentile
optimization technique with respect to a multi-class queueing system for application
in hospital Emergency Departments. The second studies the use of robust forecasting
techniques in improving developing countries’ vaccine supply chains via (1) an inno-
vative outside of cold chain policy and (2) a district-managed approach to inventory
control. Both of these research application areas utilize data-driven approaches that
feature learning and pessimism-controlled robustness.Dissertation/ThesisDoctoral Dissertation Industrial Engineering 201
Determining The Optimal Order Picking Batch Size In Single Aisle Warehouses
This work aims at investigating the influence of picking batch size to average time in
system of orders in a one-aisle warehouse under the assumption that order arrivals follow a
Poisson process and items are uniformly distributed over the aisle's length. We model this
problem as an M/G[k]/1 queue in which orders are served in batches of exactly orders. The
average time in system of the M/G[k]/1 queue is difficult to obtain for general service
times. To circumvent this obstacle, we perform an extensive numerical experiment on the
average time in system of the model when the service time is deterministic (M/D[k]/1) or
exponentially distributed (M/M[k]/1). These results are then compared with the corresponding
times in system of the actual model taken from simulation runs. A variance analysis is
carried out and its result elicits that the M/D/[k]/1 queue is a very good approximation for
the average time in system of orders. Correspondingly, the optimal picking batch size of the
real system ca
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