61091746
Resource Fairness and Composability of Cryptographic Protocols
We introduce the notion of resource-fair protocols. Informally, this property states that if one party learns the output of the protocol, then so can all other parties, as long as they expend roughly the same amount of resources. As opposed to similar previously proposed definitions, our definition follows the standard simulation paradigm and enjoys strong composability properties. In particular, our definition is similar to the security definition in the universal composability (UC) framework, but works in a model that allows any party to request additional resources from the environment to deal with dishonest parties that may prematurely abort. 
 
In this model we specify the ideally fair functionality as allowing parties to “invest resources” in return for outputs, but in such an event offering all other parties a fair deal. (The formulation of fair dealings is kept independent of any particular functionality, by defining it using a “wrapper.”) Thus, by relaxing the notion of fairness, we avoid a well-known impossibility result for fair multi-party computation with corrupted majority; in particular, our definition admits constructions that tolerate arbitrary number of corruptions. We also show that, as in the UC framework, protocols in our framework may be arbitrarily and concurrently composed. 
 
Turning to constructions, we define a “commit-prove-fair-open” functionality and design an efficient resource-fair protocol that securely realizes it, using a new variant of a cryptographic primitive known as “time-lines.” With (the fairly wrapped version of) this functionality we show that some of the existing secure multi-party computation protocols can be easily transformed into resource-fair protocols while preserving their security.
2422439
The IOC algorithm: efficient many-class non-parametric classification for high-dimensional data
This paper is about a variant of k nearest neighbor classification on large many-class high dimensional datasets.K nearest neighbor remains a popular classification technique, especially in areas such as computer vision, drug activity prediction and astrophysics. Furthermore, many more modern classifiers, such as kernel-based Bayes classifiers or the prediction phase of SVMs, require computational regimes similar to k-NN. We believe that tractable k-NN algorithms therefore continue to be important.This paper relies on the insight that even with many classes, the task of finding the majority class among the k nearest neighbors of a query need not require us to explicitly find those k nearest neighbors. This insight was previously used in (Liu et al., 2003) in two algorithms called KNS2 and KNS3 which dealt with fast classification in the case of two classes. In this paper we show how a different approach, IOC (standing for the International Olympic Committee) can apply to the case of n classes where n > 2.IOC assumes a slightly different processing of the datapoints in the neighborhood of the query. This allows it to search a set of metric trees, one for each class. During the searches it is possible to quickly prune away classes that cannot possibly be the majority.We give experimental results on datasets of up to 5.8 x 105 records and 1.5 x 103 attributes, frequently showing an order of magnitude acceleration compared with each of (i) conventional linear scan, (ii) a well-known independent SR-tree implementation of conventional k-NN and (iii) a highly optimized conventional k-NN metric tree search.
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.
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.
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.
125681
Strengthening Zero-Knowledge Protocols Using Signatures
AbstractRecently there has been an interest in zero-knowledge protocols
with stronger properties, such as concurrency, simulation
soundness, non-malleability, and universal composability.
In this paper we show a novel technique to convert a large class of
existing honest-verifier zero-knowledge protocols into ones with these
stronger properties in the common reference string model.
More precisely, our technique utilizes a signature scheme
existentially unforgeable against adaptive chosen-message attacks, and
transforms any Σ-protocol (which is honest-verifier
zero-knowledge) into a simulation sound concurrent
zero-knowledge protocol. We also introduce Ω-protocols, a variant of
Σ-protocols for which our technique further achieves the properties of non-malleability and/or universal composability. In addition to its conceptual simplicity, a main advantage of this new technique over previous ones is that
it avoids the Cook-Levin theorem, which tends to be rather
inefficient. Indeed, our technique allows for very efficient instantiation
based on the security of some efficient signature schemes and standard
number-theoretic assumptions. For instance, one instantiation of our technique yields a universally composable zero-knowledge protocol under the
Strong RSA assumption, incurring an overhead of
a small constant number of exponentiations, plus the generation of two
signatures.
6388420
Proof: A DHT-Based Peer-to-Peer Search Engine
In this paper we focus on building a large scale keyword search service over structured peer-to-peer (P2P) networks. Current state-of-the-art keyword search approaches for structured P2P systems are based on inverted list intersection. However, the biggest challenge in those approaches is that when the indices are distributed over peers, a simple query may cause a large amount of data to be transmitted over the network. We propose a new P2P keyword search scheme, called "Proof", to reduce network traffic for queries. The key idea is storing a content summary for each Web page in the inverted list, so that a query can be processed by only transmitting a small size of candidate results. Our simulation results showed that, compared with previous DHT-based P2P systems, Proof can dramatically reduce network traffic and computation time. It provides 100% precision and 90.09% recall of search results, at an acceptable cost of storage overhead, even when the number of peers and documents increases continually
16162932
AntSearch: An Ant Search Algorithm in Unstructured Peer-to-Peer Networks
The most prevalent peer-to-peer (P2P) application till today is file sharing, and unstructured P2P networks can support inherent heterogeneity of peers, are highly resilient to peers’ failures, and incur low overhead at peer arrivals and departures. Dynamic querying (DQ) is a new flooding technique which could estimate a proper time-to-live (TTL) value for a query flooding by estimating the popularity of the searched files, and retrieve sufficient results under controlled flooding range for reducing network traffic. Recent researches show that a large amount of peers in the P2P file sharing system are the free-riders, and queries are seldom hit by those peers. The free-riders problem causes a large amount of redundant messages in the DQ-like search algorithm. In this paper, we proposed a new search algorithm, called "AntSearch", to solve the problem. In AntSearch, each peer maintains its hit rate of previous queries, and records a list of pheromone values of its immediate neighbors. Based on the pheromone values, a query is only flooded to those peers which are not likely to be the free-riders. Our simulation results show that, compared with DQ and its enhanced algorithm DQ+, the AntSearch algorithm averagely reduces 50% network traffic at almost the same search latency as DQ+, while retrieving sufficient results for a query with a given required number of results.
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.
3129932
An efficient method to find area clusters with constraints using grid index structure
This work presents an efficient method to find area clusters satisfying certain constraints. The method uses a grid index structure to find them by examining five cell, 8-connected neighborhood. The hierarchical clustering technique is applied for preprocessing the grid with similar value distribution into the same clusters. This preprocessing helps reduce the computational time and eliminate the possible outlier grid cells. The constraints are average value range, minimum area size and minimum missing data ratio. The method is implemented for a remote sensing data, MOD08/spl I.bar/M3, which is MODIS (MODerate resolution Imaging Spectroradiometer) level 3 monthly atmospheric product.
21367461
A feasible method to find areas with constraints using hierarchical depth-first clustering
Addresses a reliable, feasible method to find geographical areas with constraints using hierarchical depth-first clustering. The method involves multi-level hierarchical clustering with a depth-first strategy, depending on whether the area of each cluster satisfies the given constraints. The attributes used in the hierarchical clustering are the coordinates of the grid data points. The constraints are an average value range and the minimum size of an area with a small proportion of missing data points. Convex-hull and point-in-polygon algorithms are involved in examining the constraint satisfaction. The method is implemented for an Earth science data set for vegetation studies - the Normalized Difference Vegetation Index (NVDI).