12565844
An Investigation of Practical Approximate Nearest Neighbor Algorithms
This paper concerns approximate nearest neighbor searching algorithms, which have become increasingly important, especially in high dimensional perception areas such as computer vision, with dozens of publications in recent years. Much of this enthusiasm is due to a successful new approximate nearest neighbor approach called Locality Sensitive Hashing (LSH). In this paper we ask the question: can earlier spatial data structure approaches to exact nearest neighbor, such as metric trees, be altered to provide approximate answers to proximity queries and if so, how? We introduce a new kind of metric tree that allows overlap: certain datapoints may appear in both the children of a parent. We also introduce new approximate k-NN search algorithms on this structure. We show why these structures should be able to exploit the same random-projection-based approximations that LSH enjoys, but with a simpler algorithm and perhaps with greater efficiency. We then provide a detailed empirical evaluation on five large, high dimensional datasets which show up to 31-fold accelerations over LSH. This result holds true throughout the spectrum of approximation levels.
454393
Private Keyword-Based Push and Pull with Applications to Anonymous Communication
We propose a new keyword-based Private Information Retrieval (PIR) model that allows private modification of the database from which information is requested. In our model, the database is distributed over n servers, any one of which can act as a transparent interface for clients. We present protocols that support operations for accessing data, focusing on privately appending labelled records to the database (push) and privately retrieving the next unseen record appended under a given label (pull). The communication complexity between the client and servers is independent of the number of records in the database (or more generally, the number of previous push and pull operations) and of the number of servers. Our scheme also supports access control oblivious to the database servers by implicitly including a public key in each push, so that only the party holding the private key can retrieve the record via pull. To our knowledge, this is the first system that achieves the following properties: private database modification, private retrieval of multiple records with the same keyword, and oblivious access control. We also provide a number of extensions to our protocols and, as a demonstrative application, an unlinkable anonymous communication service using them.
5253044
Towards the classical communication complexity of entanglement distillation protocols with incomplete information
Entanglement is an essential resource for quantum communication and quantum computation, similar to shared random bits in the classical world. Entanglement distillation extracts nearly-perfect entanglement from imperfect entangled state. The classical communication complexity of these protocols is the minimal amount of classical information that needs to be exchanged for the conversion. In this paper, we focus on the communication complexity of protocols that operate with incomplete information, i.e., where the inputs are mixed states and/or prepared adversarially. We consider three models of imperfect entanglement, namely, the bounded measurement model, the depolarization model, and the fidelity model. We describe these models as well as the motivations for studying them. For the bounded measurement model and the depolarization model, we prove tight and almost-tight bounds on the output quality of non-interactive protocols. For the fidelity model, we prove a lower bound that matches the upper bound given by Ambainis et al., and thus completely characterizes communication complexity of entanglement distillation protocols for this model. Our result also suggests the optimality of the BB84 protocol in terms of communication complexity. We emphasize that although some of the results appear intuitively straightforward, their proofs are not. In fact, two novel techniques are developed for proving these results. We believe that these techniques are of independent interests, too.
895559
Co-Training and Expansion: Towards Bridging Theory and Practice
Co-training is a method for combining labeled and unlabeled data when examples can be thought of as containing two distinct sets of features. It has had a number of practical successes, yet previous theoretical analyses have needed very strong assumptions on the data that are unlikely to be satisfied in practice. 
 
In this paper, we propose a much weaker "expansion" assumption on the underlying data distribution, that we prove is sufficient for iterative co-training to succeed given appropriately strong PAC-learning algorithms on each feature set, and that to some extent is necessary as well. This expansion assumption in fact motivates the iterative nature of the original co-training algorithm, unlike stronger assumptions (such as independence given the label) that allow a simpler one-shot co-training to succeed. We also heuristically analyze the effect on performance of noise in the data. Predicted behavior is qualitatively matched in synthetic experiments on expander graphs.
5335881
On statistical query sampling and NMR quantum computing
We introduce a "statistical query sampling" model, in which the goal of an algorithm is to produce an element in a hidden set S/spl sube/{0,1}/sup n/ with reasonable probability. The algorithm gains information about S through oracle calls (statistical queries), where the algorithm submits a query function g(/spl middot/) and receives an approximation to Pr/sub x/spl isin/S/[g(x)=1]. We show how this model is related to NMR quantum computing, in which only statistical properties of an ensemble of quantum systems can be measured, and in particular to the question of whether one can translate standard quantum algorithms to the NMR setting without putting all of their classical postprocessing into the quantum system. Using Fourier analysis techniques developed in the related context of statistical query learning, we prove a number of lower bounds (both information-theoretic and cryptographic) on the ability of algorithms to produce an x/spl isin/S, even when the set S is fairly simple. These lower bounds point out a difficulty in efficiently applying NMR quantum computing to algorithms such as Shor's and Simon's algorithm that involve significant classical postprocessing. We also explicitly relate the notion of statistical query sampling to that of statistical query learning.
1745284
On Simulation-Sound Trapdoor Commitments
We study the recently introduced notion of a simulation-sound trapdoor commitment (SSTC) scheme. In this paper, we present a new, simpler definition for an SSTC scheme that admits more efficient constructions and can be used in a larger set of applications. Specifically, we show how to construct SSTC schemes from any one-way functions, and how to construct very efficient SSTC schemes based on specific number-theoretic assumptions. We also show how to construct simulation-sound, non-malleable, and universally-composable zero-knowledge protocols using SSTC schemes, yielding, for instance, the most efficient universally-composable zero-knowledge protocols known. Finally, we explore the relation between SSTC schemes and non-malleable commitment schemes by presenting a sequence of implication and separation results, which in particular imply that SSTC schemes are non-malleable.
26832202
Approximate search engine optimization for directory service
Today, in many practical e-commerce systems, the real stored data usually are short strings, such as names, addresses, or other information. Searching data within these short strings is not the same as searching within longer strings. General search engines try their best to scan all long strings (or articles) quickly, and find out the places that match the search conditions. Some great online search algorithms (such as "agrep" as used inside glimpse, or "cgrep " as used inside compressed indices, or 'NR-grep') are proposed for searching without any indices in the sub-linear time O(n). However, for short strings (n is small), the practical performance of algorithms of O(n) and O(n) are much the same. Therefore, suitable indices are necessary to optimize the performance of the search engine. On the other hand, directory services are more and more important because of its optimization for searching data. The data stored in directory servers are almost short strings. The approximate search engine for directory service must take the properties of short strings into considerations. In our previous research, we have designed one approximate search engine especially for short strings by using filters to filter out the possible short strings, and then checking for the answers. However the performance of the previous search engine needs to be enhanced. In this paper, we propose new architecture and algorithm to optimize the performance of searching for directory service.
7799230
Passage feedback with IRIS
We compare a user-defined passage feedback (pf) system to a document feedback (df) system. Df employed the adaptive linear model for retrieval, while pf used weighted query expansion based on positive and negative feedback. Twenty-four searchers performed the same six tasks in varying search and system-order per TREC-8 guidelines. We hypothesized that pf, which featured interactive query expansion, would outperform df, which relied on automatic query expansion. Initial analysis appeared to reject this hypothesis, as df showed slightly higher overall performance than pf. However, analysis by system-order groups indicates only the first pf use had lower performance. These data suggest that pf was more difficult to learn than df, though the second pf use yielded competitive performance. If performance of pf is indeed affected by learning, an improved pf system with usability enhancements may prove to be an effective mechanism for interactive information retrieval.
29113389
Approximate string matching in LDAP based on edit distance
As the E-Commerce rapidly grows up, searching data is almost necessary in every application. Approximate string matching problems play a very important role to search with errors. Against these problems "Edit distance" and "Soundex" are two common techniques, especially the latter one is a "sound-like" method and had been applied to the LDAP server. Nevertheless, it is not adequate for certain situations especially when we perform the symbol matching (as in DNA); it doesn't make sense to use the "sound-like" method. On the other hand, "Edit distance" has a clear definition and also is widely used in many fields of application. Since the design of LDAP server is optimized for reading, applying edit distance technique to LDAP server has the problem of lowering speed. In this paper we design efficient data structures and an algorithm to solve the speed problem, and furthermore we use three filter conditions [1] based on the n-gram technique to achieve a well filter performance. Finally we also demonstrate experimentally the benefits of applying our algorithm and its limitations.
40685825
Value Range Queries on Earth Science Data via Histogram Clustering
Remote sensing data as well as ground-based and model output data about the Earth system can be very large in volume. On the other hand, in order to use the data efficiently, scientists need to search for data based on not only metadata but also actual data values. To answer value range queries by scanning very large volumes of data is obviously unrealistic. This article studies a clustering technique on histograms of data values on predefined cells to index the cells. Through this index system, the so-called statistical range queries can be answered quickly and approximately together with an accuracy assessment. Examples of using this technique for Earth science data sets are given in this article.
11911666
Secure online examination architecture based on distributed firewall
Online (Web-based) examination is an effective solution for mass education evaluation. However, due to the incomplete of network security, students can communicate with each other, and we can't prevent the cheating. Therefore, keeping the security of a online examination has become an important issue. This paper focuses on how to implement a secure environment for online-examination in the general academic network environment without the need of special network topologies and hardware devices. It not only reduces the system administrator's load, but also enhances the system flexibility to fit every teacher's needs. We use (1) the distributed firewall techniques to control the network packets of all machines, and (2) the centralized security policy management to control the security policies for all machines. Beside the above mentioned, we also design some mechanisms to prevent the possible network attacks and cheating, which enhance the security of the online examination environment.