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.
2552624
Approximate NN queries on Streams with Guaranteed Error/performance Bounds
In data stream applications, data arrive continuously and can only be scanned once as the query processor has very limited memory (relative to the size of the stream) to work with. Hence, queries on data streams do not have access to the entire data set and query answers are typically approximate. While there have been many studies on the k Nearest Neighbors (kNN) problem in conventional multi-dimensional databases, the solutions cannot be directly applied to data streams for the above reasons. In this paper, we investigate the kNN problem over data streams. We first introduce the e-approximate kNN (ekNN) problem that finds the approximate kNN answers of a query point Q such that the absolute error of the k-th nearest neighbor distance is bounded by e. To support ekNN queries over streams, we propose a technique called DISC (aDaptive Indexing on Streams by space-filling Curves). DISC can adapt to different data distributions to either (a) optimize memory utilization to answer ekNN queries under certain accuracy requirements or (b) achieve the best accuracy under a given memory constraint. At the same time, DISC provide efficient updates and query processing which are important requirements in data stream applications. Extensive experiments were conducted using both synthetic and real data sets and the results confirm the effectiveness and efficiency of DISC.
49680
High-dimensional kNN joins with incremental updates
The k Nearest Neighbor (kNN) join operation associates each data object in one data set with its k nearest neighbors from the same or a different data set. The kNN join on high-dimensional data (high-dimensional kNN join) is a very expensive operation. Existing high-dimensional kNN join algorithms were designed for static data sets and therefore cannot handle updates efficiently. In this article, we propose a novel kNN join method, named kNNJoin+, which supports efficient incremental computation of kNN join results with updates on high-dimensional data. As a by-product, our method also provides answers for the reverse kNN queries with very little overhead. We have performed an extensive experimental study. The results show the effectiveness of kNNJoin+ for processing high-dimensional kNN joins in dynamic workloads.
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.
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.
36437069
iDistance Techniques
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.
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.
28180483
Using Time Decompositions to Analyze PubMed Abstracts
Constructing time decompositions of time stamped documents is an important step for uncovering temporal relationships and trends of keywords and topics contained in the document set. This paper describes the use of time decompositions to extract temporal information from a small set of PubMed abstracts related to the Wnt signaling pathway. A time decomposition of the document set is constructed to identify temporal information such as keywords/topics significant in some time interval and to also identify temporal progression of the significant keywords. Keywords were assigned temporal significance values using two different measure functions based on notions of entropy and ratio. It is shown how optimal lossy decompositions of the document set are effective in reducing noise both in terms of the number of keywords as well as in terms of smoothing out the temporal progressions of keywords. Several optimal lossy decompositions for the document set are constructed and it is shown that the temporal information captured by an optimal lossy decomposition increases as its size (number of intervals) increases
10170990
Tweaking TBE/IBE to PKE Transforms with Chameleon Hash Functions
We present two transforms to acquire chosen ciphertext security from tag based techniques. The first one requires the separability of underlying primitives. By separability, informally, we mean the encryption algorithm has special structures and can process the identity and the message independently. Compared with generic transforms [8],it significantly reduces the ciphertext size overhead with only marginal computation cost. Compared with [11], the only known technique which directly achieves chosen ciphertext secure public key encryption from separable identity based primitives, it only requires selective-Tag/ID security of underlying primitives. Our second transform is less efficient but performs generically. Both transforms preserve the public verifiability of underlying primitives, and can be extended to hierarchical identity based encryption (HIBE) and threshold settings. As an independent interest, we also investigate the security requirements of chameleon hash functions to build strongly unforgeable one-time signatures.