15662668
Orthogonality between Key Privacy and Data Privacy, Revisited
Key privacy is a notion regarding the privacy of the owner of a public key, which has important applications in building (receiver) anonymous channels, or privacy-enhanced authentication/signature schemes. Key privacy is considered to be an orthogonal (i.e., independent), notion from data privacy, while the key privacy of many public key encryption schemes has not been explored, though their data privacy is comparatively well understood. In this paper, we study key privacy of many practical encryption schemes and identify evidences that key privacy is not comparable to data privacy. We also formalize key privacy in the plaintext checking attack model and point out some generic transforms to enhance the key privacy of an encryption scheme. Interestingly, these well-known techniques have been used to enhance data security. Finally, we give detailed security analyses on the signed hashed-ElGamal encryption [27] in the random oracle model, of both key privacy and data security against chosen ciphertext attack. Again, this specific example supports our claim on the relation of two notions.
39624890
Strong Anonymous Signatures
The notion of anonymous signatures has recently been formalized by [18], which captures an interesting property that a digital signature can sometimes hide the identity of the signer, if the message is hidden from the verifier. However, in many practical applications, e.g., an anonymous paper review system mentioned in [18], the message for anonymous authentication is actually known to the verifier. This implies that the effectiveness of previous anonymous signatures may be unjustified in these applications. In this paper, we extend the previous models, and develop a related primitive called strong anonymous signatures. For strong anonymous signatures, the identity of the signer remains secret even if the challenge message is chosen by an adversary. We then demonstrate some efficient constructions and prove their security in our model.
7981345
Similarity search: a matching based approach
Similarity search is a crucial task in multimedia retrieval and data mining. Most existing work has modelled this problem as the nearest neighbor (NN) problem, which considers the distance between the query object and the data objects over a fixed set of features. Such an approach has two drawbacks: 1) it leaves many partial similarities uncovered; 2) the distance is often affected by a few dimensions with high dissimilarity. To overcome these drawbacks, we propose the k-n-match problem in this paper.The k-n-match problem models similarity search as matching between the query object and the data objects in n dimensions, where n is a given integer smaller than dimensionality d and these n dimensions are determined dynamically to make the query object and the data objects returned in the answer set match best. The k-n-match query is expected to be superior to the kNN query in discovering partial similarities, however, it may not be as good in identifying full similarity since a single value of n may only correspond to a particular aspect of an object instead of the entirety. To address this problem, we further introduce the frequent k-n-match problem, which finds a set of objects that appears in the k-n-match answers most frequently for a range of n values. Moreover, we propose search algorithms for both problems. We prove that our proposed algorithm is optimal in terms of the number of individual attributes retrieved, which is especially useful for information retrieval from multiple systems. We can also apply the proposed algorithmic strategy to achieve a disk based algorithm for the (frequent) k-n-match query. By a thorough experimental study using both real and synthetic data sets, we show that: 1) the k-n-match query yields better result than the kNN query in identifying similar objects by partial similarities; 2) our proposed method (for processing the frequent k-n-match query) outperforms existing techniques for similarity search in terms of both effectiveness and efficiency.
967678
iDistance: An adaptive B+-tree based indexing method for nearest neighbor search
In this article, we present an efficient B+-tree based indexing method, called iDistance, for K-nearest neighbor (KNN) search in a high-dimensional metric space. iDistance partitions the data based on a space- or data-partitioning strategy, and selects a reference point for each partition. The data points in each partition are transformed into a single dimensional value based on their similarity with respect to the reference point. This allows the points to be indexed using a B+-tree structure and KNN search to be performed using one-dimensional range search. The choice of partition and reference points adapts the index structure to the data distribution.We conducted extensive experiments to evaluate the iDistance technique, and report results demonstrating its effectiveness. We also present a cost model for iDistance KNN search, which can be exploited in query optimization.
207469
A Generalized Global Convergence Theory of Projection-Type Neural Networks for Optimization
The projection-type neural networks $\tau \frac{dx}{dt}=-x+P_{\Omega }(x-\Lambda (t)\partial ^{0}E(x))$are generic and useful models for solving the constrained optimization problems min {E(x)|x∈Ω}. In the existing convergence/ stability analysis, the most are deduced based on the assumptions that E is uniformly or strictly convex and Ω is box-shaped. In this talk we present a generalized theory on convergence/stability of the networks. In the general setting that E is only convex and Ω is any closed bounded convex set, it is shown the global convergence/asymptotic stability of the networks in a specified sense. The presented theory sharpens and generalizes the existing results, and, consequently, underlies the applicability of the neural networks for a broader type of optimization problems.
10078749
RoleVAT: Visual Assessment of Practical Need for Role Based Access Control
Role based access control (RBAC) is a powerful security administration concept that can simplify permission assignment management. Migration to and maintenance of RBAC requires role engineering, the identification of a set of roles that offer administrative benefit. However, establishing that RBAC is desirable in a given enterprise is lacking in current role engineering processes. To help identify the practical need for RBAC, we propose RoleVAT, a Role engineering tool for the Visual Assessment of user and permission Tendencies. User and permission clusters can be visually identified as potential user groups or roles. The benefit and impact of this visual analysis in enterprise environments is discussed and demonstrated through testing on real life as well as synthetic datasets. Our experimental results show the effectiveness of RoleVAT as well as interesting user and role tendencies in real enterprise environments.
15606709
Mining distribution change in stock order streams
Detecting changes in stock prices is a well known problem in finance with important implications for monitoring and business intelligence. Forewarning of changes in stock price, can be made by the early detection of changes in the distributions of stock order numbers. In this paper, we address the change detection problem for streams of stock order numbers and propose a novel incremental detection algorithm. Our algorithm gains high accuracy and low delay by employing a natural Poisson distribution assumption about the nature of stock order streams. We establish that our algorithm is highly scalable and has linear complexity. We also experimentally demonstrate its effectiveness for detecting change points, via experiments using both synthetic and real-world datasets.