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We study the problem of enhancing Entity Resolution (ER) with the help of crowdsourcing. ER is the problem of clustering records that refer to the same real-world entity and can be an extremely di cult process for computer algorithms alone. For example, figuring out which images refer to the same person can be a hard task for computers, but an easy one for… (More)

- Peter Lofgren, Siddhartha Banerjee, Ashish Goel
- WSDM
- 2016

We present new algorithms for Personalized PageRank estimation and Personalized PageRank search. First, for the problem of estimating Personalized PageRank (PPR) from a source distribution to a target node, we present a new bidirectional estimator with simple yet strong guarantees on correctness and performance, and 3x to 8x speedup over existing estimators… (More)

We propose a new algorithm, FAST-PPR, for computing personalized PageRank: given start node <i>s</i> and target node <i>t</i> in a directed graph, and given a threshold δ, it computes the Personalized PageRank π_s(t) from <i>s</i> to <i>t</i>, guaranteeing that the relative error is small as long π<sub><i>s</i></sub>(<i>t</i>) > δ.… (More)

- Peter Lofgren, Nicholas Hopper
- WPES
- 2011

We introduce Faust, a solution to the "anonymous blacklisting problem:" allow an anonymous user to prove that she is authorized to access an online service such that if the user misbehaves, she retains her anonymity but will be unable to authenticate in future sessions. Faust uses no trusted third parties and is one to two orders of magnitude more efficient… (More)

Anonymous blacklisting schemes allow online service providers to prevent future anonymous access by abusive users while preserving the privacy of all anonymous users (both abusive and non-abusive). The first scheme proposed for this purpose was Nymble, an extremely efficient scheme based only on symmetric primitives; however, Nymble relies on trusted third… (More)

- Vasilis Verroios, Peter Lofgren, Hector Garcia-Molina
- SIGMOD Conference
- 2015

Latency is a critical factor when using a crowdsourcing platform to solve a problem like entity resolution or sorting. In practice, most frameworks attempt to reduce latency by heuristically splitting a budget of questions into rounds, so that after each round the answers are analyzed and new questions are selected. We focus on one of the most extensively… (More)

- Peter Lofgren, Ashish Goel
- ArXiv
- 2013

Personalalized PageRank uses random walks to determine the importance or authority of nodes in a graph from the point of view of a given source node. Much past work has considered how to compute personalized PageRank from a given source node to other nodes. In this work we consider the problem of computing personalized PageRanks to a given target node from… (More)

- Peter Lofgren, Siddhartha Banerjee
- NIPS
- 2015

We develop a new bidirectional algorithm for estimating Markov chain multi-step transition probabilities: given a Markov chain, we want to estimate the probability of hitting a given target state in ` steps after starting from a given source distribution. Given the target state t, we use a (reverse) local power iteration to construct an ‘expanded target… (More)

- Peter Lofgren, Nicholas Hopper
- Financial Cryptography
- 2011

- Hongyang Zhang, Peter Lofgren, Ashish Goel
- KDD
- 2016

We propose and analyze two algorithms for maintaining approximate Personalized PageRank (PPR) vectors on a dynamic graph, where edges are added or deleted. Our algorithms are natural dynamic versions of two known local variations of power iteration. One, Forward Push, propagates probability mass forwards along edges from a source node, while the other,… (More)