Optimizing Batch Linear Queries under Exact and Approximate Differential Privacy

Differential privacy is a promising privacy-preserving paradigm for statistical query processing over sensitive data. It works by injecting random noise into each query result, such that it is provably hard for the adversary to infer the presence or absence of any individual record from the published noisy results. The main objective in differentially private query processing is to maximize the accuracy of the query results, while satisfying the privacy guarantees. Previous work, notably \cite{LHR+10}, has suggested that with an appropriate strategy, processing a batch of correlated queries as a whole achieves considerably higher accuracy than answering them individually. However, to our knowledge there is currently no practical solution to find such a strategy for an arbitrary query batch; existing methods either return strategies of poor quality (often worse than naive methods) or require prohibitively expensive computations for even moderately large domains. Motivated by this, we propose low-rank mechanism (LRM), the first practical differentially private technique for answering batch linear queries with high accuracy. LRM works for both exact (i.e., $\epsilon$-) and approximate (i.e., ($\epsilon$, $\delta$)-) differential privacy definitions. We derive the utility guarantees of LRM, and provide guidance on how to set the privacy parameters given the user's utility expectation. Extensive experiments using real data demonstrate that our proposed method consistently outperforms state-of-the-art query processing solutions under differential privacy, by large margins.Comment: ACM Transactions on Database Systems (ACM TODS). arXiv admin note: text overlap with arXiv:1212.230

DRS: Dynamic Resource Scheduling for Real-Time Analytics over Fast Streams

In a data stream management system (DSMS), users register continuous queries, and receive result updates as data arrive and expire. We focus on applications with real-time constraints, in which the user must receive each result update within a given period after the update occurs. To handle fast data, the DSMS is commonly placed on top of a cloud infrastructure. Because stream properties such as arrival rates can fluctuate unpredictably, cloud resources must be dynamically provisioned and scheduled accordingly to ensure real-time response. It is quite essential, for the existing systems or future developments, to possess the ability of scheduling resources dynamically according to the current workload, in order to avoid wasting resources, or failing in delivering correct results on time. Motivated by this, we propose DRS, a novel dynamic resource scheduler for cloud-based DSMSs. DRS overcomes three fundamental challenges: (a) how to model the relationship between the provisioned resources and query response time (b) where to best place resources; and (c) how to measure system load with minimal overhead. In particular, DRS includes an accurate performance model based on the theory of \emph{Jackson open queueing networks} and is capable of handling \emph{arbitrary} operator topologies, possibly with loops, splits and joins. Extensive experiments with real data confirm that DRS achieves real-time response with close to optimal resource consumption.Comment: This is the our latest version with certain modificatio

Big Changes in How Students are Tested

For the past decade, school accountability has relied on tests for which the essential format has remained unchanged. Educators are familiar with the yearly testing routine: schools are given curriculum frameworks, teachers use the frameworks to guide instruction, students take one big test at year’s end which relies heavily upon multiple-choice bubble items, and then school leaders wait anxiously to find out whether enough of their students scored at or above proficiency to meet state standards. All this will change with the adoption of Common Core standards. Testing and accountability aren’t going away. Instead, they are developing and expanding in ways that aim to address many of the present shortcomings of state testing routines. Most importantly, these new tests will be computer-based. As such, they will potentially shorten testing time, increase tests’ precision, and provide immediate feedback to students and teachers

Functional Mechanism: Regression Analysis under Differential Privacy

\epsilon-differential privacy is the state-of-the-art model for releasing sensitive information while protecting privacy. Numerous methods have been proposed to enforce epsilon-differential privacy in various analytical tasks, e.g., regression analysis. Existing solutions for regression analysis, however, are either limited to non-standard types of regression or unable to produce accurate regression results. Motivated by this, we propose the Functional Mechanism, a differentially private method designed for a large class of optimization-based analyses. The main idea is to enforce epsilon-differential privacy by perturbing the objective function of the optimization problem, rather than its results. As case studies, we apply the functional mechanism to address two most widely used regression models, namely, linear regression and logistic regression. Both theoretical analysis and thorough experimental evaluations show that the functional mechanism is highly effective and efficient, and it significantly outperforms existing solutions.Comment: VLDB201

DMI Report 21-17 Including a dynamic Greenland Ice Sheet in the EC-Earth global climate model

Recent observations have indicated rapidly increasing mass loss from the Greenland Ice Sheet. To explore the interactions and feedbacks of the ice sheets in the climate system, it is important to develop coupled climate-ice sheet models. The integration of an ice sheet model in a global model is challenging, and, currently, relatively few climate models include a two-way coupling to a dynamical ice sheet model. In this work package, we have continued developing the coupled ice sheet-climate model system comprising the global climate model EC-Earth and the Parallel Ice Sheet Model (PISM) for Greenland. The new model system, EC-Earth3-GrIS, is upgraded to include the recent model versions, EC-Earth3 and PISM version 1.2. In addition, a new module has been developed to handle the exchange of information between the ice sheet model and EC-Earth using the OASIS3- MCT software interface. The new module reads output from the ice sheet model and exchanges the fields with the relevant EC-Earth components. The ice sheet mask and topography are provided to the atmosphere and land surface components. The heat and freshwater fluxes from basal melt and ice discharge are provided to the ocean module via the runoff-mapper that routes surface runoff into the ocean. The new module also prepares the forcing fields for the ice sheet model, i.e., subsurface temperature and surface mass balance. These fields are calculated in EC- Earth3 using a land ice surface parameterization, developed explicitly for the Greenland ice sheet. The parameterization contains a responsive snow and ice albedo scheme and includes land ice characteristics in the calculation of heat and energy transfer at the surface. Experiments with and without the land ice surface parameterization have been carried out for preindustrial and present-day conditions to assess the influence of the surface parameterization on the calculated surface mass balance. The results show that the ice sheet responds stronger and more realistically to forcing changes when the new surface parameterization is used. Besides the model development, the results from experiments with the first model version, EC- Earth-PISM, have been analyzed. These results stress that a decent surface scheme with a responsive snow albedo scheme is necessary for investigating mass balance changes of the Greenland Ice Sheet. Overall, our results indicate that the feedbacks induced by the interactive ice sheet have a significant influence on Arctic climate change under warming conditions. In warm scenarios where the CO2 level is raised to four times the preindustrial level, the coupled model has a colder Arctic surface, a fresher ocean, and more sea-ice in winter

Low-Rank Mechanism: Optimizing Batch Queries under Differential Privacy

Differential privacy is a promising privacy-preserving paradigm for statistical query processing over sensitive data. It works by injecting random noise into each query result, such that it is provably hard for the adversary to infer the presence or absence of any individual record from the published noisy results. The main objective in differentially private query processing is to maximize the accuracy of the query results, while satisfying the privacy guarantees. Previous work, notably the matrix mechanism, has suggested that processing a batch of correlated queries as a whole can potentially achieve considerable accuracy gains, compared to answering them individually. However, as we point out in this paper, the matrix mechanism is mainly of theoretical interest; in particular, several inherent problems in its design limit its accuracy in practice, which almost never exceeds that of naive methods. In fact, we are not aware of any existing solution that can effectively optimize a query batch under differential privacy. Motivated by this, we propose the Low-Rank Mechanism (LRM), the first practical differentially private technique for answering batch queries with high accuracy, based on a low rank approximation of the workload matrix. We prove that the accuracy provided by LRM is close to the theoretical lower bound for any mechanism to answer a batch of queries under differential privacy. Extensive experiments using real data demonstrate that LRM consistently outperforms state-of-the-art query processing solutions under differential privacy, by large margins.Comment: VLDB201

Shifts in methanogenic community composition and methane fluxes along the degradation of discontinuous permafrost

Published version. Also available at http://dx.doi.org/10.3389/fmicb.2015.00356The response of methanogens to thawing permafrost is an important factor for the global greenhouse gas budget. We tracked methanogenic community structure, activity, and abundance along the degradation of sub-Arctic palsa peatland permafrost. We observed the development of pronounced methane production, release, and abundance of functional (mcrA) methanogenic gene numbers following the transitions from permafrost (palsa) to thaw pond structures. This was associated with the establishment of a methanogenic community consisting both of hydrogenotrophic (Methanobacterium, Methanocellales), and potential acetoclastic (Methanosarcina) members and their activity. While peat bog development was not reflected in significant changes of mcrA copy numbers, potential methane production, and rates of methane release decreased. This was primarily linked to a decline of potential acetoclastic in favor of hydrogenotrophic methanogens. Although palsa peatland succession offers similarities with typical transitions from fen to bog ecosystems, the observed dynamics in methane fluxes and methanogenic communities are primarily attributed to changes within the dominant Bryophyta and Cyperaceae taxa rather than to changes in peat moss and sedge coverage, pH and nutrient regime. Overall, the palsa peatland methanogenic community was characterized by a few dominant operational taxonomic units (OTUs). These OTUs seem to be indicative for methanogenic species that thrive in terrestrial organic rich environments. In summary, our study shows that after an initial stage of high methane emissions following permafrost thaw, methane fluxes, and methanogenic communities establish that are typical for northern peat bogs