8312634
Probabilistic Continuous Update Scheme in Location Dependent Continuous Queries
It is difficult to maintain the exact location of mobile objects due to the limited resources in a mobile network. A consequence of this problem is that the update cost for a location-dependent continuous query for moving objects can be quite high using traditional methods. In this paper, we propose a probabilistic update method to maintain the fidelity of the query results without incurring significant update cost. Our scheme makes use of two types of updates, one to keep the uncertainty of the mobile object’s position within a specific confidence interval, and the other using probability that the moving object’s location uncertainty will affect the query result as the threshold to decide whether an update should be generated or not. The effectiveness of our approach is demonstrated using a series of simulation experiments.
7935679
Deferrable Scheduling for Temporal Consistency: Schedulability Analysis and Overhead Reduction
The deferrable scheduling algorithm for fixed priority transactions (DS-FP) is demonstrated to be a very effective approach for minimizing real-time update transaction workload while maintaining temporal validity of real-time data. This paper examines the schedulability of deferrable scheduling and presents a sufficient condition. While its online scheduling overhead is a concern, we propose a hyperperiod based DS-FP approach satisfying the temporal validity constraint with low overhead. The algorithm, namely DEferrable Scheduling with Hyperperiod by Schedule Adjustment (DESH-SA), adjusts the DS-FP schedule in an interval so that the adjusted schedule in the interval can be repeated infinitely. Our experiments demonstrate that DESH-SA can reduce scheduling overhead of DS-FP and also achieve update workload near optimal
7999297
A deferrable scheduling algorithm for real-time transactions maintaining data freshness
Periodic update transaction model has been used to maintain freshness (or temporal validity) of real-time data. Period and deadline assignment has been the main focus in the past studies such as the more-less scheme by Xiong and Ramamrithan (2004) in which update transactions are guaranteed by the deadline monotonic scheduling algorithm by Leung and Whitehead (1982) to complete by their deadlines. In this paper, we propose a novel algorithm, namely deferrable scheduling, for minimizing imposed workload while maintaining temporal validity of real-time data. In contrast to previous work, update transactions scheduled by the deferrable scheduling algorithm follow a sporadic task model. The deferrable scheduling algorithm exploits the semantics of temporal validity constraint of real-time data by judiciously deferring the sampling times of update transaction jobs as late as possible. We present a theoretical analysis of its processor utilization, which is verified in our experiments. Our experimental results also demonstrate that the deferrable scheduling algorithm is a very effective approach, and it significantly outperforms the more-less scheme in terms of reducing processor workload
9276774
Privacy-preservation for gradient descent methods
Gradient descent is a widely used paradigm for solving many optimization problems. Stochastic gradient descent performs a series of iterations to minimize a target function in order to reach a local minimum. In machine learning or data mining, this function corresponds to a decision model that is to be discovered. The gradient descent paradigm underlies many commonly used techniques in data mining and machine learning, such as neural networks, Bayesian networks, genetic algorithms, and simulated annealing. To the best of our knowledge, there has not been any work that extends the notion of privacy preservation or secure multi-party computation to gradient-descent-based techniques. In this paper, we propose a preliminary approach to enable privacy preservation in gradient descent methods in general and demonstrate its feasibility in specific gradient descent methods.
15268724
Privacy-Preserving Genetic Algorithms for Rule Discovery
Decision tree induction algorithms generally adopt a greedy approach to select attributes in order to optimize some criteria at each iteration of the tree induction process. When a decision tree has been constructed, a set of decision rules may be correspondingly derived. Univariate decision tree induction algorithms generally yield the same tree regardless of how many times it is induced from the same training data set. Genetic algorithms have been shown to discover a better set of rules, albeit at the expense of efficiency. In this paper, we propose a protocol for secure genetic algorithms for the following scenario: Two parties, each holding an arbitrarily partitioned data set, seek to perform genetic algorithms to discover a better set of rules without disclosing their own private data. The challenge for privacy-preserving genetic algorithms is to allow the two parties to securely and jointly evaluate the fitness value of each chromosome using each party's private data but without compromising their data privacy. We propose a new protocol to address this challenge that is correct and secure. The proposed protocol is not only privacy-preserving at each iteration of the genetic algorithm, the intermediate results generated at each iteration do not compromise the data privacy of the participating parties.
10475402
Human face orientation detection using power spectrum based measurements
This work presents a new approach to human face orientation detection. We start the work with establishing a frequency domain model to analyze the orientation distribution of the oriented patterns in an image, according to the power spectrum of its bi-valued image. This model is derived from the perspective of the rendering rules in classical computer graphics. On the assumptions of the crossed distribution rule and symmetry rule of facial textures, we use the model above to define a series of measurements, called power spectrum based measurements, to quickly determine the orientation of a human face. The experimental results on MIT, ORL and BiolD face databases demonstrate our approach is effective and promising.
15030913
Automatic Real-Time Barcode Localization in Complex Scenes
Many applications exist for automatically finding and reading barcodes in complex scenes with a camera. The key problem is to search barcodes in a complex scene and supply them for a reading subsystem. However, illumination, rotation, perspective distortion and multiple barcodes circumstances make barcode localization difficult. By jointly analyzing texture and shape, we propose a realtime barcode localization method that is robust to the above problems. A user only needs to put a barcode in front of a camera at around 15 cm for common Web cameras, and the system then automatically locates and decodes the barcode. This paper only focuses at automatic localization. We will demonstrate the complete system live at the conference.
12065835
Privacy-Preserving Self-Organizing Map
Privacy-preserving data mining seeks to allow the cooperative execution of data mining algorithms while preserving the data privacy of each party concerned. In recent years, many data mining algorithms have been enhanced with privacy-preserving feature: decision tree induction, frequent itemset counting, association analysis, k-means clustering, support vector machine, Naive Bayes classifier, Bayesian networks, and so on. In this paper, we propose a protocol for privacy-preserving self-organizing map for vertically partitioned data involving two parties. Self-organizing map (SOM) is awidely used algorithmfor transforming data sets to a lower dimensional space to facilitate visualization. The challenges in preserving data privacy in SOM are (1) to securely discover the winner neuron from data privately held by two parties; (2) to securely update weight vectors of neurons; and (3) to securely determine the termination status of SOM. We propose protocols to address the above challenges. We prove that these protocols are correct and privacy-preserving. Also, we prove that the intermediate results generated by these protocols do not violate the data privacy of the participating parties.