11052169
Privacy-Preserving Classification of Customer Data without Loss of Accuracy
Privacy has become an increasingly important issue in data mining. In this paper, we consider a scenario in which a data miner surveys a large number of customers to learn classiflcation rules on their data, while the sensitive attributes of these customers need to be protected. Solutions have been proposed to address this problem using randomization techniques. Such solutions exhibit a tradeofi of accuracy and privacy: the more each customer’s private information is protected, the less accurate result the miner obtains; conversely, the more accurate the result, the less privacy for the customers. In this paper, we propose a simple cryptographic approach that is e‐cient even in a many-customer setting, provides strong privacy for each customer, and does not lose any accuracy as the cost of privacy. Our key technical contribution is a privacy-preserving method that allows a data miner to compute frequencies of values or tuples of values in the customers’ data, without revealing the privacy-sensitive part of the data. Unlike general-purpose cryptographic protocols, this method requires no interaction between customers, and each customer only needs to send a single ∞ow of communication to the data miner. However, we are still able to ensure that nothing about the sensitive data beyond the desired frequencies is revealed to the data miner. To illustrate the power of our approach, we use our frequency mining computation to obtain a privacypreserving naive Bayes classifler learning algorithm. Initial experimental results demonstrate the practical e‐ciency of our solution. We also suggest some other applications of privacy-preserving frequency mining.
17249203
Privacy-Preserving Queries on Encrypted Data
Data confidentiality is a major concern in database systems. Encryption is a useful tool for protecting the confidentiality of sensitive data. However, when data is encrypted, performing queries becomes more challenging. In this paper, we study efficient and provably secure methods for queries on encrypted data stored in an outsourced database that may be susceptible to compromise. Specifically, we show that, in our system, even if an intruder breaks into the database and observes some interactions between the database and its users, he only learns very little about the data stored in the database and the queries performed on the data. Our work consists of several components. First, we consider databases in which each attribute has a finite domain and give a basic solution for certain kinds of queries on such databases. Then, we present two enhanced solutions, one with a stronger security guarantee and the other with accelerated queries. In addition to providing proofs of our security guarantees, we provide empirical performance evaluations. Our experiments demonstrate that our solutions are fast on large-sized real data.
17075655
Privacy-enhancing k-anonymization of customer data
In order to protect individuals' privacy, the technique of k-anonymization has been proposed to de-associate sensitive attributes from the corresponding identifiers. In this paper, we provide privacy-enhancing methods for creating k-anonymous tables in a distributed scenario. Specifically, we consider a setting in which there is a set of customers, each of whom has a row of a table, and a miner, who wants to mine the entire table. Our objective is to design protocols that allow the miner to obtain a k-anonymous table representing the customer data, in such a way that does not reveal any extra information that can be used to link sensitive attributes to corresponding identifiers, and without requiring a central authority who has access to all the original data. We give two different formulations of this problem, with provably private solutions. Our solutions enhance the privacy of k-anonymization in the distributed scenario by maintaining end-to-end privacy from the original customer data to the final k-anonymous results.
6993641
A Fast Fixed-Point Algorithm for Two-Class Discriminative Feature Extraction
We propose a fast fixed-point algorithm to improve the Relevant Component Analysis (RCA) in two-class cases. Using an objective function that maximizes the predictive information, our method is able to extract more than one discriminative component of data for two-class problems, which cannot be accomplished by classical Fisher's discriminant analysis. After prewhitening the data, we apply Newton's optimization method which automatically chooses the learning rate in the iterative training of each component. The convergence of the iterative learning is quadratic, i.e. much faster than the linear optimization by gradient. methods. Empirical tests presented in the paper show that feature extraction using the new method resembles RCA for low-dimensional ionosphere data and significantly outperforms the latter in efficiency for high-dimensional facial image data.
44572255
Approximated Classification in Interactive Facial Image Retrieval
For databases of facial images, where each subject is depicted in only one or a few images, the query precision of interactive retrieval suffers from the problem of extremely small class sizes. A potential way to address this problem is to employ automatic even though imperfect classification on the images according to some high level concepts. In this paper we point out that significant improvement in terms of the occurrence of the first subject hit is feasible only when the classifiers are of sufficient accuracy. In this work Support Vector Machines (SVMs) are incorporated in order to obtain high accuracy for classifying the imbalanced data. We also propose an automatic method to choose the penalty factor of training error and the width parameter of the radial basis function used in training the SVM classifiers. More significant improvement in the speed of retrieval is feasible with small classes than with larger ones. The results of our experiments suggest that the first subject hit can be obtained two to five times faster for semantic classes such as “black persons” or “eyeglass-wearing persons”.
8920735
Partition-based decision heuristics for image computation using SAT and BDDs
Methods based on Boolean satisfiability (SAT) typically use a conjunctive normal form (CNF) representation of the Boolean formula, and exploit the structure of the given problem through use of various decision heuristics and implication methods. We propose a new decision heuristic based on separator-set induced partitioning of the underlying CNF graph. It targets those variables whose choice generates clause partitions with disjoint variable supports. This can potentially improve performance of SAT applications by decomposing the problem dynamically within the search. In the context of a recently proposed image computation method combining SAT and BDDs, this results in simpler BDD subproblems. We provide algorithms for CNF partitioning - one based on a clause-variable dependency matrix, and another based on standard hypergraph partitioning techniques, and also for the use of partitioning information in decision heuristics for SAT. The effectiveness of the proposed partition-based heuristic is shown with practical results for reachability analysis of benchmark sequential circuits.
15468202
Hybridisation of Particle Swarm Optimization and Fast Evolutionary Programming
Particle swarm optimization (PSO) and fast evolutionary programming (FEP) are two widely used population-based optimisation algorithms. The ideas behind these two algorithms are quite different. While PSO is very efficient in local converging to an optimum due to its use of directional information, FEP is better at global exploration and finding a near optimum globally. This paper proposes a novel hybridisation of PSO and FEP, i.e., fast PSO (FPSO), where the strength of PSO and FEP is combined. In particular, the ideas behind Gaussian and Cauchy mutations are incorporated into PSO. The new FPSO has been tested on a number of benchmark functions. The preliminary results have shown that FPSO outperformed both PSO and FEP significantly.
5056026
Rate distortion optimization in the scalable video coding
This paper discusses the rate distortion optimization (RDO) issue in the scalable video coding. It is different from that in the non-scalable video coding because a scalable codec usually presents the decoded video within a certain range of the quality, frame rate and resolution. Therefore, this paper first analyzes the problem from a general model and gives a common description by the Lagrangian formula. Secondly, an algorithm is proposed to solve the above optimization problem at a specific scalable codec: the H.26L-based PFGS. At the same time, two analytic formulae are derived to ascertain the parameters /spl lambda/ in this algorithm. The experimental results show that the proposed algorithm significantly improves the coding efficiency of the H.26L-based PFGS. Further studies on this problem will be quite significant and challenging.