36437069
iDistance Techniques
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.
16280855
Indexing Fast Moving Objects for kNN Queries Based on Nearest Landmarks
With the rapid advancements in positioning technologies such as the Global Positioning System (GPS) and wireless communications, the tracking of continuously moving objects has become more convenient. However, this development poses new challenges to database technology since maintaining up-to-date information regarding the location of moving objects incurs an enormous amount of updates. Existing indexes can no longer keep up with the high update rate while providing speedy retrieval at the same time. This study aims to improve k nearest neighbor (kNN) query performance while reducing update costs. Our approach is based on an important observation that queries usually occur around certain places or spatial landmarks of interest, called reference points. We propose the Reference-Point-based tree (RP-tree), which is a two-layer index structure that indexes moving objects according to reference points. Experimental results show that the RP-tree achieves significant improvement over the TPR-tree.
7607256
Making the pyramid technique robust to query types and workloads
The effectiveness of many existing high-dimensional indexing structures is limited to specific types of queries and workloads. For example, while the Pyramid technique and the iMinMax are efficient for window queries, the iDistance is superior for kNN queries. We present a new structure, called the P/sup +/-tree, that supports both window queries and kNN queries under different workloads efficiently. In the P/sup +/-tree, a B/sup +/-tree is employed to index the data points as follows. The data space is partitioned into subspaces based on clustering, and points in each subspace are mapped onto a single dimensional space using the Pyramid technique, and stored in the B/sup +/ -tree. The crux of the scheme lies in the transformation of the data which has two crucial properties. First, it maps each subspace into a hypercube so that the Pyramid technique can be applied. Second, it shifts the cluster center to the top of the pyramid, which is the case that the Pyramid technique works very efficiently. We present window and kNN query processing algorithms for the P/sup +/-tree. Through an extensive performance study, we show that the P/sup +/-tree has considerable speedup over the Pyramid technique and the iMinMax for window queries and outperforms the iDistance for kNN queries.
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.
7619448
Generalized multidimensional data mapping and query processing
Multidimensional data points can be mapped to one-dimensional space to exploit single dimensional indexing structures such as the B+-tree. In this article we present a Generalized structure for data Mapping and query Processing (GiMP), which supports extensible mapping methods and query processing. GiMP can be easily customized to behave like many competent indexing mechanisms for multi-dimensional indexing, such as the UB-Tree, the Pyramid technique, the iMinMax, and the iDistance. Besides being an extendible indexing structure, GiMP also serves as a framework to study the characteristics of the mapping and hence the efficiency of the indexing scheme. Specifically, we introduce a metric called mapping redundancy to characterize the efficiency of a mapping method in terms of disk page accesses and analyze its behavior for point, range and kNN queries. We also address the fundamental problem of whether an efficient mapping exists and how to define such a mapping for a given data set.
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.
20077755
Efficient Identity-Based Encryption with Tight Security Reduction
In this paper, we present an efficient variant of the Boneh-Franklin scheme that achieves a tight security reduction. Our scheme is basically an IBE scheme under two keys, one of which is randomly chosen and given to the user. It can be viewed as a continuation of an idea introduced by Katz and Wang; however, unlike the Katz-Wang variant, our scheme is quite efficient, as its ciphertext size is roughly comparable to that of the original full Boneh-Franklin scheme. The security of our scheme can be based on either the gap bilinear Diffie-Hellman (GBDH) or the decisional bilinear Diffie-Hellman (DBDH) assumptions.
17395662
On the Security of Cryptosystems with All-or-Nothing Transform
An AONT is an efficiently computable transform with two properties. Given all the bits of its output, it is easy to retrieve the message. On the other hand, if sufficiently many bits of the output are missing, it is computationally infeasible for an polynomial-time adversary to learn any information about the message. The natural intuition then may be deduced that if an secure AONT is used in a cryptosystem, the whole system will be secure as long as sufficiently many bits are “protected”. However, we show this is not enough. Our results are three-fold: First we answer an open problem raised in [6], showing that previous definitions are not sufficient to guarantee a provably secure cryptosystem with strong data privacy, namely, indistinguishability against chosen ciphertext attack (IND-CCA). Second, we give a new definition to AONT, showing this definition suffices to guarantee an AONT integrated with any encryption functions to acquire IND-CCA secure cryptosystems. Third, we give concrete constructions that satisfy the new definition.
15452878
A generic construction of useful client puzzles
Denial of Service (DoS) attacks are serious threats for network societies. For dealing with DoS attacks, Jakobsson and Juels first proposed the notion of useful client puzzles (UCPs) which simultaneously decrease servers' burden and increase clients'. In ACM CCS'04, Diament, Lee, Keromytis, and Yung introduced the decryption-based UCPs. In this paper, we give a general framework for constructing such UCPs which is based on identity-based cryptography along with well-analyzed symmetric key authenticated encryption techniques (without random oracles). By using this framework, we can flexibly construct various UCPs according to different types of system requirements. We also give some instantiations: the first is a UCP based on the Boneh-Boyen Identity based encryption scheme, with provable security in the standard model. Another one is a UCP based on the Boneh-Gentry-Hamburg identity-based encryption scheme, which doesn't require pairings.
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.