18050648
Optimized algorithms for predictive range and KNN queries on moving objects
There have been many studies on management of moving objects recently. Most of them try to optimize the performance of predictive window queries. However, not much attention is paid to two other important query types: the predictive range query and the predictive k nearest neighbor query. In this article, we focus on these two types of queries. The novelty of our work mainly lies in the introduction of the Transformed Minkowski Sum, which can be used to determine whether a moving bounding rectangle intersects a moving circular query region. This enables us to use the traditional tree traversal algorithms to perform range and kNN searches. We theoretically show that our algorithms based on the Transformed Minkowski Sum are optimal in terms of the number of tree node accesses. We also experimentally verify the effectiveness of our technique and show that our algorithms outperform alternative approaches.
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.
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.
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.
36437069
iDistance Techniques
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.
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.
711993
On the Security of Multiple Encryption or CCA-security+CCA-security=CCA-security?
In a practical system, a message is often encrypted more than once by different encryptions, here called multiple encryption, to enhance its security. Additionally, new features may be achieved by multiple encrypting a message, such as the key-insulated cryptosystems and anonymous channels. Intuitively, a multiple encryption should remain “secure”, whenever there is one component cipher unbreakable in it. In NESSIE’s latest Portfolio of recommended cryptographic primitives (Feb. 2003), it is suggested to use multiple encryption with component ciphers based on different assumptions to acquire long term security. However, in this paper we show this needs careful discussion, especially, this may not be true according to adaptive chosen ciphertext attack (CCA), even with all component ciphers CCA-secure. We define an extended model of (standard) CCA called chosen ciphertext attack for multiple encryption (ME-CCA) emulating partial breaking of assumptions, and give constructions of multiple encryption satisfying ME-CCA-security. We further relax CCA by introducing weak ME-CCA (ME-wCCA) and study the relations among these definitions, proving ME-wCCA-security can be acquired by combining IND-CCA-secure component ciphers together. We then apply these results to key-insulated cryptosystem.
45094967
Efficient Algorithms for Constructing Time Decompositions of Time Stamped Documents
Identifying temporal information of topics from a document set typically involves constructing a time decomposition of the time period associated with the document set. In an earlier work, we formulated several metrics on a time decomposition, such as size, information loss, and variability, and gave dynamic programming based algorithms to construct time decompositions that are optimal with respect to these metrics. Computing information loss values for all subintervals of the time period is central to the computation of optimal time decompositions. This paper proposes several algorithms to assist in more efficiently constructing an optimal time decomposition. More efficient, parallelizable algorithms for computing loss values are described. An efficient top-down greedy heuristic to construct an optimal time decomposition is also presented. Experiments to study the performance of this greedy heuristic were conducted. Although lossy time decompositions constructed by the greedy heuristic are suboptimal, they seem to be better than the widely used uniform length decompositions.
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
2046540
Generic Combination of Public Key Encryption with Keyword Search and Public Key Encryption
In this paper, we study the problem of secure integrating public key encryption with keyword search (PEKS) with public key data encryption (PKE). We argue the previous security model is not complete regarding keyword privacy and the previous constructions are secure only in the random oracle model. We solve these problems by first defining a new security model, then give a generic construction which is secure in the new security model without random oracles. Our construction is based on secure PEKS and tag-KEM/DEM schemes and achieves modular design. We also point out some applications and extensions for our construction. For example, instantiate our construction with proper components, we have a concrete scheme without random oracles, whose performance is even competitive to the previous schemes with random oracles.