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70
Revocation and Tracing Schemes for Stateless Receivers
, 2001
"... Abstract. We deal with the problem of a center sending a message to a group of users such that some subset of the users is considered revoked and should not be able to obtain the content of the message. We concentrate on the stateless receiver case, where the users do not (necessarily) update their ..."
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Cited by 248 (5 self)
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Abstract. We deal with the problem of a center sending a message to a group of users such that some subset of the users is considered revoked and should not be able to obtain the content of the message. We concentrate on the stateless receiver case, where the users do not (necessarily) update their state from session to session. We present a framework called the SubsetCover framework, which abstracts a variety of revocation schemes including some previously known ones. We provide sufficient conditions that guarantees the security of a revocation algorithm in this class. We describe two explicit SubsetCover revocation algorithms; these algorithms are very flexible and work for any number of revoked users. The schemes require storage at the receiver of log N and 1 2 log2 N keys respectively (N is the total number of users), and in order to revoke r users the required message lengths are of r log N and 2r keys respectively. We also provide a general traitor tracing mechanism that can be integrated with any SubsetCover revocation scheme that satisfies a “bifurcation property”. This mechanism does not need an a priori bound on the number of traitors and does not expand the message length by much compared to the revocation of the same set of traitors. The main improvements of these methods over previously suggested methods, when adopted to the stateless scenario, are: (1) reducing the message length to O(r) regardless of the coalition size while maintaining a single decryption at the user’s end (2) provide a seamless integration between the revocation and tracing so that the tracing mechanisms does not require any change to the revocation algorithm.
Generalized binary search
 In Proceedings of the 46th Allerton Conference on Communications, Control, and Computing
, 2008
"... This paper addresses the problem of noisy Generalized Binary Search (GBS). GBS is a wellknown greedy algorithm for determining a binaryvalued hypothesis through a sequence of strategically selected queries. At each step, a query is selected that most evenly splits the hypotheses under consideratio ..."
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Cited by 58 (0 self)
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This paper addresses the problem of noisy Generalized Binary Search (GBS). GBS is a wellknown greedy algorithm for determining a binaryvalued hypothesis through a sequence of strategically selected queries. At each step, a query is selected that most evenly splits the hypotheses under consideration into two disjoint subsets, a natural generalization of the idea underlying classic binary search. GBS is used in many applications, including fault testing, machine diagnostics, disease diagnosis, job scheduling, image processing, computer vision, and active learning. In most of these cases, the responses to queries can be noisy. Past work has provided a partial characterization of GBS, but existing noisetolerant versions of GBS are suboptimal in terms of query complexity. This paper presents an optimal algorithm for noisy GBS and demonstrates its application to learning multidimensional threshold functions. 1
The Karmed Dueling Bandits Problem
, 2009
"... We study a partialinformation onlinelearning problem where actions are restricted to noisy comparisons between pairs of strategies (also known as bandits). In contrast to conventional approaches that require the absolute reward of the chosen strategy to be quantifiable and observable, our setting ..."
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Cited by 29 (7 self)
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We study a partialinformation onlinelearning problem where actions are restricted to noisy comparisons between pairs of strategies (also known as bandits). In contrast to conventional approaches that require the absolute reward of the chosen strategy to be quantifiable and observable, our setting assumes only that (noisy) binary feedback about the relative reward of two chosen strategies is available. This type of relative feedback is particularly appropriate in applications where absolute rewards have no natural scale or are difficult to measure (e.g., userperceived quality of a set of retrieval results, taste of food, product attractiveness), but where pairwise comparisons are easy to make. We propose a novel regret formulation in this setting, as well as present an algorithm that achieves (almost) informationtheoretically optimal regret bounds (up to a constant factor).
Randomized Shellsort: A simple oblivious sorting algorithm
 In Proceedings 21st ACMSIAM Symposium on Discrete Algorithms (SODA
, 2010
"... In this paper, we describe a randomized Shellsort algorithm. This algorithm is a simple, randomized, dataoblivious version of the Shellsort algorithm that always runs in O(n log n) time and succeeds in sorting any given input permutation with very high probability. Taken together, these properties ..."
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Cited by 28 (8 self)
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In this paper, we describe a randomized Shellsort algorithm. This algorithm is a simple, randomized, dataoblivious version of the Shellsort algorithm that always runs in O(n log n) time and succeeds in sorting any given input permutation with very high probability. Taken together, these properties imply applications in the design of new efficient privacypreserving computations based on the secure multiparty computation (SMC) paradigm. In addition, by a trivial conversion of this Monte Carlo algorithm to its Las Vegas equivalent, one gets the first version of Shellsort with a running time that is provably O(n log n) with very high probability. 1
LargeScale Validation and Analysis of Interleaved Search Evaluation
, 2012
"... Interleaving is an increasingly popular technique for evaluating information retrieval systems based on implicit user feedback. While a number of isolated studies have analyzed how this technique agrees with conventional offline evaluation approaches and other online techniques, a complete picture o ..."
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Cited by 23 (3 self)
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Interleaving is an increasingly popular technique for evaluating information retrieval systems based on implicit user feedback. While a number of isolated studies have analyzed how this technique agrees with conventional offline evaluation approaches and other online techniques, a complete picture of its efficiency and effectiveness is still lacking. In this paper we extend and combine the body of empirical evidence regarding interleaving, and provide a comprehensive analysis of interleaving using data from two major commercial search engines and a retrieval system for scientific literature. In particular, we analyze the agreement of interleaving with manual relevance judgments and observational implicit feedback measures, estimate the statistical efficiency of interleaving, and explore the relative performance of different interleaving variants. We also show how to learn improved creditassignment functions for clicks that further increase the sensitivity of interleaving.
Sorting and Searching in the Presence of Memory Faults (without Redundancy)
 Proc. 36th ACM Symposium on Theory of Computing (STOC’04
, 2004
"... We investigate the design of algorithms resilient to memory faults, i.e., algorithms that, despite the corruption of some memory values during their execution, are able to produce a correct output on the set of uncorrupted values. In this framework, we consider two fundamental problems: sorting and ..."
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Cited by 21 (4 self)
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We investigate the design of algorithms resilient to memory faults, i.e., algorithms that, despite the corruption of some memory values during their execution, are able to produce a correct output on the set of uncorrupted values. In this framework, we consider two fundamental problems: sorting and searching. In particular, we prove that any O(n log n) comparisonbased sorting algorithm can tolerate at most O((n log n) ) memory faults. Furthermore, we present one comparisonbased sorting algorithm with optimal space and running time that is resilient to O((n log n) ) faults. We also prove polylogarithmic lower and upper bounds on faulttolerant searching.
Sample complexity for 1bit compressed sensing and sparse classification
 in Proc. Intl. Symp. on Information Theory (ISIT
, 2010
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Robust polynomials and quantum algorithms
 Theory of Computing Systems
"... Abstract. We define and study the complexity of robust polynomials for Boolean functions and the related faulttolerant quantum decision trees, where input bits are perturbed by noise. We show that, in contrast to the classical model of Feige et al., every Boolean function can be computed by O(n) qu ..."
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Cited by 19 (5 self)
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Abstract. We define and study the complexity of robust polynomials for Boolean functions and the related faulttolerant quantum decision trees, where input bits are perturbed by noise. We show that, in contrast to the classical model of Feige et al., every Boolean function can be computed by O(n) quantum queries even in the model with noise. This implies, for instance, the somewhat surprising result that every Boolean function has robust degree bounded by O(n). 1
Using the crowd for topk and groupby queries
 In ICDT
, 2013
"... Groupby and topk are fundamental constructs in database queries. However, the criteria used for grouping and ordering certain types of data – such as unlabeled photos clustered by the same person ordered by age – are difficult to evaluate by machines. In contrast, these tasks are easy for humans t ..."
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Cited by 18 (5 self)
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Groupby and topk are fundamental constructs in database queries. However, the criteria used for grouping and ordering certain types of data – such as unlabeled photos clustered by the same person ordered by age – are difficult to evaluate by machines. In contrast, these tasks are easy for humans to evaluate and are therefore natural candidates for being crowdsourced. We study the problem of evaluating topk and groupby queries using the crowd to answer either type or value questions. Given two data elements, the answer to a type question is “yes ” if the elements have the same type and therefore belong to the same group or cluster; the answer to a value question orders the two data elements. The assumption here is that there is an underlying ground truth, but that the answers returned by the crowd may sometimes be erroneous. We formalize the problems of topk and groupby in the crowdsourced setting, and give efficient algorithms that are guaranteed to achieve good results with high probability. We analyze the crowdsourced cost of these algorithms in terms of the total number of type and value questions, and show that they are essentially the best possible. We also show that fewer questions are needed when values and types are correlated, or when the error model is one in which the error decreases as the distance between the two elements in the sorted order increases.