Efficient Authentication of Outsourced String Similarity Search

Cloud computing enables the outsourcing of big data analytics, where a third party server is responsible for data storage and processing. In this paper, we consider the outsourcing model that provides string similarity search as the service. In particular, given a similarity search query, the service provider returns all strings from the outsourced dataset that are similar to the query string. A major security concern of the outsourcing paradigm is to authenticate whether the service provider returns sound and complete search results. In this paper, we design AutoS3, an authentication mechanism of outsourced string similarity search. The key idea of AutoS3 is that the server returns a verification object VO to prove the result correctness. First, we design an authenticated string indexing structure named MBtree for VO construction. Second, we design two lightweight authentication methods named VS2 and EVS2 that can catch the service provider various cheating behaviors with cheap verification cost. Moreover, we generalize our solution for top k string similarity search. We perform an extensive set of experiment results on real world datasets to demonstrate the efficiency of our approach

Frequency-hiding Dependency-preserving Encryption for Outsourced Databases

The cloud paradigm enables users to outsource their data to computationally powerful third-party service providers for data management. Many data management tasks rely on the data dependencies in the outsourced data. This raises an important issue of how the data owner can protect the sensitive information in the outsourced data while preserving the data dependencies. In this paper, we consider functional dependency FD, an important type of data dependency. We design a FD-preserving encryption scheme, named F2, that enables the service provider to discover the FDs from the encrypted dataset. We consider the frequency analysis attack, and show that the F2 encryption scheme can defend against the attack under Kerckhoff's principle with provable guarantee. Our empirical study demonstrates the efficiency and effectiveness of F2

Integrity Authentication for SQL Query Evaluation on Outsourced Databases: A Survey

Spurred by the development of cloud computing, there has been considerable recent interest in the Database-as-a-Service (DaaS) paradigm. Users lacking in expertise or computational resources can outsource their data and database management needs to a third-party service provider. Outsourcing, however, raises an important issue of result integrity: how can the client verify with lightweight overhead that the query results returned by the service provider are correct (i.e., the same as the results of query execution locally)? This survey focuses on categorizing and reviewing the progress on the current approaches for result integrity of SQL query evaluation in the DaaS model. The survey also includes some potential future research directions for result integrity verification of the outsourced computations

Time-varying Bang-bang Property of Minimal Controls for Approximately Null-controllable Heat Equations

In this paper, optimal time control problems and optimal target control problems are studied for the approximately null-controllable heat equations. Compared with the existed results on these problems, the boundary of control variables are not constants but time varying functions. The time-varying bang-bang property for optimal time control problem, and an equivalence theorem for optimal control problem and optimal target problem are obtained.Comment: 13 page

Magnetic dipole-dipole interaction induced by the electromagnetic field

We give a derivation for the indirect interaction between two magnetic dipoles induced by the quantized electromagnetic field. It turns out that the interaction between permanent dipoles directly returns to the classical form; the interaction between transition dipoles does not directly return to the classical result, yet returns in the short-distance limit. In a finite volume, the field modes are highly discrete, and both the permanent and transition dipole-dipole interactions are changed. For transition dipoles, the changing mechanism is similar with the Purcell effect, since only a few number of nearly resonant modes take effect in the interaction mediation; for permanent dipoles, the correction comes from the boundary effect: if the dipoles are placed close to the boundary, the influence is strong, otherwise, their interaction does not change too much from the free space case.Comment: 8 pages, 2 figure

Long-Term Stability Analysis of Power Systems with Wind Power Based on Stochastic Differential Equations: Model Development and Foundations

In this paper, the variable wind power is incorporated into the dynamic model for long-term stability analysis. A theory-based method is proposed for power systems with wind power to conduct long-term stability analysis, which is able to provide accurate stability assessments with fast simulation speed. Particularly, the theoretical foundation for the proposed approximation approach is presented. The accuracy and efficiency of the method are illustrated by several numerical examples.Comment: The paper has been submitted to IEEE Transactions on Sustainable Energ

A Framework for Dynamic Stability Analysis of Power Systems with Volatile Wind Power

We propose a framework employing stochastic differential equations to facilitate the long-term stability analysis of power grids with intermittent wind power generations. This framework takes into account the discrete dynamics which play a critical role in the long-term stability analysis, incorporates the model of wind speed with different probability distributions, and also develops an approximation methodology (by a deterministic hybrid model) for the stochastic hybrid model to reduce the computational burden brought about by the uncertainty of wind power. The theoretical and numerical studies show that a deterministic hybrid model can provide an accurate trajectory approximation and stability assessments for the stochastic hybrid model under mild conditions. In addition, we discuss the critical cases that the deterministic hybrid model fails and discover that these cases are caused by a violation of the proposed sufficient conditions. Such discussion complements the proposed framework and methodology and also reaffirms the importance of the stochastic hybrid model when the system operates close to its stability limit.Comment: The paper has been accepted by IEEE Journal on Emerging and Selected Topics in Circuits and System

How isolated is enough for an "isolated" system in statistical mechanics?

Irreversible processes are frequently adopted to account for the entropy increase in classical thermodynamics. However, the corresponding physical origins are not always clear, e.g. in a free expansion process, a typical model in textbooks. In this letter, we study the entropy change during free expansion for a particle with the thermal de Broglie wavelength ($\lambda_{T}$) in a one-dimensional square trap with size $L$. By solely including quantum dephasing as an irreversible process, we recover classical result of entropy increase in the classical region ($L\gg\lambda_{T}$), while predict prominent discrepancies in the quantum region ($L\ll\lambda_{T}$) because of non-equilibrium feature of trapped atoms after expansion. It is interesting to notice that the dephasing, though absent in classical system, is critical to clarify mysteries in classical thermodynamics.Comment: 5 pages, 4 figure

Cost-efficient Data Acquisition on Online Data Marketplaces for Correlation Analysis

Incentivized by the enormous economic profits, the data marketplace platform has been proliferated recently. In this paper, we consider the data marketplace setting where a data shopper would like to buy data instances from the data marketplace for correlation analysis of certain attributes. We assume that the data in the marketplace is dirty and not free. The goal is to find the data instances from a large number of datasets in the marketplace whose join result not only is of high-quality and rich join informativeness, but also delivers the best correlation between the requested attributes. To achieve this goal, we design DANCE, a middleware that provides the desired data acquisition service. DANCE consists of two phases: (1) In the off-line phase, it constructs a two-layer join graph from samples. The join graph consists of the information of the datasets in the marketplace at both schema and instance levels; (2) In the online phase, it searches for the data instances that satisfy the constraints of data quality, budget, and join informativeness, while maximize the correlation of source and target attribute sets. We prove that the complexity of the search problem is NP-hard, and design a heuristic algorithm based on Markov chain Monte Carlo (MCMC). Experiment results on two benchmark datasets demonstrate the efficiency and effectiveness of our heuristic data acquisition algorithm

Tensor effects on gap evolution of N=40 from non-relativistic and relativistic mean-field theory

Tensor effects on the N=40 gap evolution of N=40 isotones are studied by employing the Skyrme-Hartree-Fock-Bogoliubov (SHFB) and relativistic Hartree-Fock-Bogoliubov (RHFB) theories. The results with and without the inclusion of the tensor component are compared with the experimental data. When the tensor force is included, both of the two different approaches are found to give the same trend and agree with the experimental one, which indicates the necessity of introducing the tensor force in the evolution of N=40 subshell and on the other hand the reliability of the methods. Furthermore, it is shown that the gap evolution is primarily determined by the corresponding tensor contributions from $\pi$ and $\rho$-tensor coupling in the relativistic framework.Comment: 4 pages, 3 figure