5135938
Privacy preserving database application testing
Traditionally, application software developers carry out their tests on their own local development databases. However, such local databases usually have only a small number of sample data and hence cannot simulate satisfactorily a live environment, especially in terms of performance and scalability testing. On the other hand, the idea of testing applications over live production databases is increasingly problematic in most situations primarily due to the fact that such use of live production databases has the potential to expose sensitive data to an unauthorized tester and to incorrectly update information in the underlying database. In this paper, we investigate techniques to generate mock databases for application software testing without revealing any confidential information from the live production databases. Specifically, we will design mechanisms to create the deterministic rule set R, non-deterministic rule set N R, and statistic data set S for a live production database. We will then build a security Analyzer which will process the triplet <R',N R',S'> together with security requirements (security policy) and output a new triplet <R',N R',S'> The security Analyzer will guarantee that no confidential information could be inferred from the new triplet <R',N R',S'> The mock database generated from this new triplet can simulate the live environment for testing purpose, while maintaining the privacy of data in the original database.
10131509
Using Approximation Hardness to Achieve Dependable Computation
Redundancy has been utilized to achieve fault tolerant computation and to achieve reliable communication in networks of processors. These techniques can only be extended to computations solely based on functions in one input in which redundant hardware or software (servers) are used to compute intermediate and end results. However, almost all practical computation systems consist of components which are based on computations with multiple inputs. Wang, Desmedt, and Burmester have used AND/OR graphs to model this scenario. Roughly speaking, an AND/OR graph is a directed graph with two types of vertices, labeled ∧-vertices and ∨-vertices. In this case, processors which need all their inputs in order to operate could be represented by ∧-vertices, whereas processors which can choose one of their "redundant" inputs could be represented by ∨-vertices. In this paper, using the results for hardness of approximation and optimization problems, we will design dependable computation systems which could defeat as many malicious faults as possible. Specifically, assuming certain approximation hardness result, we will construct k-connected AND/OR graphs which could defeat a ck-active adversary (therefore a ck-passive adversary also) where c > 1 is any given constant. This result improves a great deal on the results for the equivalent communication problems.
5782664
A Separation of Two Randomness Concepts
In this paper we give an affirmative answer to a conjecture by Lutz (1992). That is, we will show that there is a Schnorr random sequence which is not rec-random.
16463784
Privacy Preserving Data Generation for Database Application Performance Testing
Synthetic data plays an important role in software testing. In this paper, we initiate the study of synthetic data generation models for the purpose of application software performance testing. In particular, we will discuss models for protecting privacy in synthetic data generations. Within this model, we investigate the feasibility and techniques for privacy preserving synthetic database generation that can be used for database application performance testing. The methodologies that we will present will be useful for general privacy preserving software performance testing.
8204880
Privacy aware data generation for testing database applications
Testing of database applications is of great importance. A significant issue in database application testing consists in the availability of representative data. In this paper, we investigate the problem of generating a synthetic database based on a-priori knowledge about a production database. Our approach is to fit general location model using various characteristics (e.g., constraints, statistics, rules) extracted from the production database and then generate the synthetic data using model learnt. The generated data is valid and similar to real data in terms of statistical distribution, hence it can be used for functional and performance testing. As characteristics extracted may contain information which may be used by attacker to derive some confidential information about individuals, we present our disclosure analysis method which applies cell suppression technique for identity disclosure analysis and perturbation for value disclosure.
41292415
Randomness, Stochasticity and Approximations
Polynomial time unsafe approximations for intractable sets were introduced by Meyer and Paterson [9] and Yesha [19], respectively. The question of which sets have optimal unsafe approximations has been investigated extensively; see, e.g., [1], [5], [15], and [17]. Recently, Wang [15], [17] showed that polynomial time random sets are neither optimally unsafe approximable nor Δ -levelable. In this paper we show that: (1) There exists a polynomial time stochastic set in the exponential time complexity class which has an optimal unsafe approximation. (2) There exists a polynomial time stochastic set in the exponential time complexity class which is Δ -levelable. The above two results answer a question asked by Ambos-Spies and Lutz [2]: What kind of natural complexity property can be characterized by p -randomness but not by p -stochasticity? Our above results also extend Ville's [13] historical result. The proof of our first result shows that, for Ville's stochastic sequence, we can find an optimal prediction function f such that we will never lose our own money betting according to f (except the money we have earned), that is to say, if at the beginning we have only $1 and we always bet $1 that the next selected bit is 1 , then we always have enough money to bet on the next bit. Our second result shows that there is a stochastic sequence for which there is a betting strategy f such that we will never lose our own money betting according to f (except the money we have earned), but there is no such optimal betting strategy. That is to say, for any such betting strategy, we can find another betting strategy which could be used to make money more quickly.
16213423
DFT-MSN: The Delay/Fault-Tolerant Mobile Sensor Network for Pervasive Information Gathering
This paper focuses on the Delay/Fault-Tolerant Mobile Sensor Network (DFT-MSN) for pervasive information gathering. We develop simple and efficient data delivery schemes tailored for DFT-MSN, which has several unique characteristics such as sensor mobility, loose connectivity, fault tolerability, delay tolerability, and buffer limit. We first study two basic approaches, namely, direct transmission and flooding. We analyze their performance by using queuing theory and statistics. Based on the analytic results that show the tradeoff between data delivery delay/ratio and transmission overhead, we introduce an optimized flooding scheme that minimizes transmission overhead in flooding. Then, we propose a simple and effective DFT-MSN data delivery scheme, which consists of two key components for data transmission and queue management, respectively. The former makes decision on when and where to transmit data messages based on the delivery probability, which reflects the likelihood that a sensor can deliver data messages to the sink. The latter decides which messages to transmit or drop based on the fault tolerance, which indicates the importance of the messages. The system parameters are carefully tuned on the basis of thorough analyses to optimize network performance. Extensive simulations are carried out for performance evaluation. Our results show that the proposed DFT-MSN data delivery scheme achieves the highest message delivery ratio with acceptable delay and transmission overhead.
12841371
Implementing the Real-Time Processes of RTPA using Real-Time Java
This paper discusses the implementation of the real-time processes of real-time process algebra (RTPA) using real-time Java. The difficulty in implementing real-time software from formal specifications comes from the fact that some realtime features expressed using a formal specification language cannot be easily transformed into a programming language. The paper shows how a combination of a real-time support library for RTPA (RTPASupportLib) and the recently developed extensions to real-time Java can overcome some of these difficulties. The strategies and techniques employed in implementing RTPA real-time features such as concurrency, interleaving, and interrupt/event/time-driven dispatching of real-time processes are presented. A number of case studies on the implementation of RTPA real-time features show that the approach is a feasible one
1536242
Substitution Delone Sets 

Abstract. Substitution Delone set families are families of Delone sets X =(X1, . . ., Xn) which satisfy the inflation functional equation
 X_i = \bigvee_{j=1}^m ( \mbox{\phvr A} (X_j) + {\cal D}_$ij$ ) , \qquad 1 \le i \le m, 
 in which A is an expanding matrix, i.e., all of the eigenvalues of A fall outside the unit circle. Here the Dij are finite sets of vectors in Rd and V denotes union that counts multiplicity. This paper characterizes families X=(X1, . . ., Xn) that satisfy an inflation functional equation, in which each Xi is a multiset (set with multiplicity) whose underlying set is discrete. It then studies the subclass of Delone set solutions, and gives necessary conditions on the coefficients of the inflation functional equation for such solutions X to exist. It relates Delone set solutions to a narrower subclass of solutions, called self-replicating multi-tiling sets, which arise as tiling sets for self-replicating multi-tilings.

15514608
Application of Relief-F feature filtering algorithm to selecting informative genes for cancer classification using microarray data
Numerous recent studies have shown that microarray gene expression data is useful for cancer classification. Classification based on microarray data is very different from previous classification problems in that the number of features (genes) greatly exceeds the number of instances (tissue samples). It has been shown that selecting a small set of informative genes can lead to improved classification accuracy. It is thus important to first apply feature selection methods prior to classification. In the machine learning field, one of the most successful feature filtering algorithms is the Relief-F algorithm. In this work, we empirically evaluate its performance on three published cancer classification data sets. We use the linear SVM and the k-NN as classifiers in the experiments, and compare the performance of Relief-F with other feature filtering methods, including Information Gain, Gain Ratio, and /spl chi//sup 2/-statistic. Using the leave-one-out cross validation, experimental results show that the performance of Relief-F is comparable with other methods.
44869762
Agent based dynamic recovery protocol in distributed databases
Recovering from node failures is a critical issue in distributed database systems. In conventional log-based recovery protocols, the nodes providing recovery service may be overburdened, especially when the recovery is resource consuming. In this paper, an agent-based dynamic recovery protocol is presented. It divides the whole recovery process into three major steps: log-recovery, agent-recovery, and synchronization. The key idea of this protocol is to cache new database operations initiated during recovery into agents. All these cached operations are then replayed independently for further recovery. The analysis indicates that the new protocol can minimize internode's dependency and improve recovery speed. As a result, system failure rate is cut down and the overall performance gets improved.