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407
Resisting Structural Reidentification in Anonymized Social Networks
, 2008
"... We identify privacy risks associated with releasing network data sets and provide an algorithm that mitigates those risks. A network consists of entities connected by links representing relations such as friendship, communication, or shared activity. Maintaining privacy when publishing networked dat ..."
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Cited by 86 (7 self)
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We identify privacy risks associated with releasing network data sets and provide an algorithm that mitigates those risks. A network consists of entities connected by links representing relations such as friendship, communication, or shared activity. Maintaining privacy when publishing networked data is uniquely challenging because an individual’s network context can be used to identify them even if other identifying information is removed. In this paper, we quantify the privacy risks associated with three classes of attacks on the privacy of individuals in networks, based on the knowledge used by the adversary. We show that the risks of these attacks vary greatly based on network structure and size. We propose a novel approach to anonymizing network data that models aggregate network structure and then allows samples to be drawn from that model. The approach guarantees anonymity for network entities while preserving the ability to estimate a wide variety of network measures with relatively little bias.
Exponentiated gradient algorithms for conditional random fields and maxmargin Markov networks
, 2008
"... Loglinear and maximummargin models are two commonlyused methods in supervised machine learning, and are frequently used in structured prediction problems. Efficient learning of parameters in these models is therefore an important problem, and becomes a key factor when learning from very large dat ..."
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Cited by 84 (2 self)
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Loglinear and maximummargin models are two commonlyused methods in supervised machine learning, and are frequently used in structured prediction problems. Efficient learning of parameters in these models is therefore an important problem, and becomes a key factor when learning from very large data sets. This paper describes exponentiated gradient (EG) algorithms for training such models, where EG updates are applied to the convex dual of either the loglinear or maxmargin objective function; the dual in both the loglinear and maxmargin cases corresponds to minimizing a convex function with simplex constraints. We study both batch and online variants of the algorithm, and provide rates of convergence for both cases. In the maxmargin case, O ( 1 ε) EG updates are required to reach a given accuracy ε in the dual; in contrast, for loglinear models only O(log (1/ε)) updates are required. For both the maxmargin and loglinear cases, our bounds suggest that the online EG algorithm requires a factor of n less computation to reach a desired accuracy than the batch EG algorithm, where n is the number of training examples. Our experiments confirm that the online algorithms are much faster than the batch algorithms in practice. We describe how the EG updates factor in a convenient way for structured prediction problems, allowing the algorithms to be
Event queries on correlated probabilistic streams (full version
, 2008
"... A major problem in detecting events in streams of data is that the data can be imprecise (e.g. RFID data). However, current stateoftheart event detection systems such as Cayuga [14], SASE [46] or SnoopIB[1], assume the data is precise. Noise in the data can be captured using techniques such as hi ..."
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Cited by 56 (15 self)
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A major problem in detecting events in streams of data is that the data can be imprecise (e.g. RFID data). However, current stateoftheart event detection systems such as Cayuga [14], SASE [46] or SnoopIB[1], assume the data is precise. Noise in the data can be captured using techniques such as hidden Markov models. Inference on these models creates streams of probabilistic events which cannot be directly queried by existing systems. To address this challenge we propose Lahar1, an event processing system for probabilistic event streams. By exploiting the probabilistic nature of the data, Lahar yields a much higher recall and precision than deterministic techniques operating over only the most probable tuples. By using a novel static analysis and novel algorithms, Lahar processes data orders of magnitude more efficiently than a naïve approach based on sampling. In this paper, we present Lahar’s static analysis and core algorithms. We demonstrate the quality and performance of our approach through experiments with our prototype implementation and comparisons with alternate methods.
An Improved Construction for Counting Bloom Filters
 14th Annual European Symposium on Algorithms, LNCS 4168
, 2006
"... Abstract. A counting Bloom filter (CBF) generalizes a Bloom filter data structure so as to allow membership queries on a set that can be changing dynamically via insertions and deletions. As with a Bloom filter, a CBF obtains space savings by allowing false positives. We provide a simple hashingbas ..."
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Cited by 54 (4 self)
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Abstract. A counting Bloom filter (CBF) generalizes a Bloom filter data structure so as to allow membership queries on a set that can be changing dynamically via insertions and deletions. As with a Bloom filter, a CBF obtains space savings by allowing false positives. We provide a simple hashingbased alternative based on dleft hashing called a dleft CBF (dlCBF). The dlCBF offers the same functionality as a CBF, but uses less space, generally saving a factor of two or more. We describe the construction of dlCBFs, provide an analysis, and demonstrate their effectiveness experimentally. 1
Less hashing, same performance: Building a better bloom filter
 In Proc. the 14th Annual European Symposium on Algorithms (ESA 2006
, 2006
"... ABSTRACT: A standard technique from the hashing literature is to use two hash functions h1(x) and h2(x) to simulate additional hash functions of the form gi(x) = h1(x) + ih2(x). We demonstrate that this technique can be usefully applied to Bloom filters and related data structures. Specifically, on ..."
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Cited by 53 (6 self)
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ABSTRACT: A standard technique from the hashing literature is to use two hash functions h1(x) and h2(x) to simulate additional hash functions of the form gi(x) = h1(x) + ih2(x). We demonstrate that this technique can be usefully applied to Bloom filters and related data structures. Specifically, only two hash functions are necessary to effectively implement a Bloom filter without any loss in the asymptotic false positive probability. This leads to less computation and potentially less need for
Beyond Bloom Filters: From Approximate Membership Checks to Approximate State Machines
 SIGCOMM '06
, 2006
"... Many networking applications require fast state lookups in a concurrent state machine, which tracks the state of a large number of flows simultaneously. We consider the question of how to compactly represent such concurrent state machines. To achieve compactness, we consider data structures for Appr ..."
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Cited by 43 (6 self)
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Many networking applications require fast state lookups in a concurrent state machine, which tracks the state of a large number of flows simultaneously. We consider the question of how to compactly represent such concurrent state machines. To achieve compactness, we consider data structures for Approximate Concurrent State Machines (ACSMs) that can return false positives, false negatives, or a “don’t know” response. We describe three techniques based on Bloom filters and hashing, and evaluate them using both theoretical analysis and simulation. Our analysis leads us to an extremely efficient hashingbased scheme with several parameters that can be chosen to trade off space, computation, and the impact of errors. Our hashing approach also yields a simple alternative structure with the same functionality as a counting Bloom filter that uses much less space. We show how ACSMs can be used for video congestion control. Using an ACSM, a router can implement sophisticated Active Queue Management (AQM) techniques for video traffic (without the need for standards changes to mark packets or change video formats), with a factor of four reduction in memory compared to fullstate schemes and with very little error. We also show that ACSMs show promise for realtime detection of P2P traffic.
Efficient Broadcasting using Network Coding
, 2008
"... We consider the problem of broadcasting in an adhoc wireless network, where all nodes of the network are sources that want to transmit information to all other nodes. Our figure of merit is energy efficiency, a critical design parameter for wireless networks since it directly affects battery life an ..."
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Cited by 37 (3 self)
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We consider the problem of broadcasting in an adhoc wireless network, where all nodes of the network are sources that want to transmit information to all other nodes. Our figure of merit is energy efficiency, a critical design parameter for wireless networks since it directly affects battery life and thus network lifetime. We prove that applying ideas from network coding allows to realize significant benefits in terms of energy efficiency for the problem of broadcasting, and propose very simple algorithms that allow to realize these benefits in practice. In particular, our theoretical analysis shows that network coding improves performance by a constant factor in fixed networks. We calculate this factor exactly for some canonical configurations. We then show that in networks where the topology dynamically changes, for example due to mobility, and where operations are restricted to simple distributed algorithms, network coding can offer improvements of a factor of log n, where n is the number of nodes in the network. We use the insights gained from the theoretical analysis to propose lowcomplexity distributed algorithms for realistic wireless adhoc scenarios, discuss a number of practical considerations, and evaluate our algorithms through packet level simulation.
Using Rank Propagation and Probabilistic Counting for LinkBased Spam Detection
 In Proceedings of the Workshop on Web Mining and Web Usage Analysis (WebKDD
, 2006
"... This paper describes a technique for automating the detection of Web link spam, that is, groups of pages that are linked together with the sole purpose of obtaining an undeservedly high score in search engines. The problem of Web spam is widespread and di#cult to solve, mostly due to the large size ..."
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Cited by 36 (13 self)
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This paper describes a technique for automating the detection of Web link spam, that is, groups of pages that are linked together with the sole purpose of obtaining an undeservedly high score in search engines. The problem of Web spam is widespread and di#cult to solve, mostly due to the large size of web collections that makes many algorithms unfeasible in practice.