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- Souptik Datta, Kanishka Bhaduri, Chris Giannella, Ran Wolff, Hillol Kargupta
- IEEE Internet Computing
- 2006

Peer-to-peer (P2P) networks are gaining popularity in many applications such as file sharing, e-commerce, and social networking, many of which deal with rich, distributed data sources that can benefit from data mining. P2P networks are, in fact, well-suited to distributed data mining (DDM), which deals with the problem of data analysis in environments with… (More)

- Joel W. Branch, Boleslaw K. Szymanski, Chris Giannella, Ran Wolff, Hillol Kargupta
- Knowledge and Information Systems
- 2006

To address the problem of unsupervised outlier detection in wireless sensor networks, we develop an approach that (1) is flexible with respect to the outlier definition, (2) computes the result in-network to reduce both bandwidth and energy consumption, (3) uses only single-hop communication, thus permitting very simple node failure detection and message… (More)

- Arik Friedman, Ran Wolff, Assaf Schuster
- VLDB J.
- 2008

In this paper we present extended definitions of k-anonymity and use them to prove that a given data mining model does not violate the k-anonymity of the individuals represented in the learning examples. Our extension provides a tool that measures the amount of anonymity retained during data mining. We show that our model can be applied to various data… (More)

- Ran Wolff, Assaf Schuster
- ICDM
- 2003

We extend the problem of association rule mining--a key data mining problem--to systems in which the database is partitioned among a very large number of computers that are dispersed over a wide area. Such computing systems include grid computing platforms, federated database systems, and peer-to-peer computing environments. The scale of these systems poses… (More)

- Assaf Schuster, Ran Wolff
- SIGMOD Conference
- 2001

Mining for associations between items in large transactional databases is a central problem in the field of knowledge discovery. When the database is partitioned among several share-nothing machines, the problem can be addressed using distributed data mining algorithms. One such algorithm, called CD, was proposed by Agrawal and Shafer in [1] and was later… (More)

- Assaf Schuster, Ran Wolff, Dan Trock
- Knowl. Inf. Syst.
- 2003

We present a new distributed association rule mining (D-ARM) algorithm that demonstrates superlinear speedup with the number of computing nodes. The algorithm is the first D-ARM algorithm to perform a single scan over the database. As such, its performance is unmatched by any previous algorithm. Scale-up experiments over standard synthetic benchmarks… (More)

- Yitzhak Birk, Idit Keidar, Liran Liss, Assaf Schuster, Ran Wolff
- PODC
- 2006

This paper focuses on <i>local</i> computations of distributed aggregation problems on fixed graphs. We define a new metric on problem instances, <i>Veracity Radius (VR)</i>, which captures the inherent possibility to compute them locally. We prove that VR yields a tight lower bound on output-stabilization time, i.e., the time until all nodes fix their… (More)

- Kanishka Bhaduri, Ran Wolff, Chris Giannella, Hillol Kargupta
- Statistical Analysis and Data Mining
- 2008

This paper offers a scalable and robust distributed algorithm for decision tree induction in large Peer-to-Peer (P2P) environments. Computing a decision tree in such large distributed systems using standard centralized algorithms can be very communication-expensive and impractical because of the synchronization requirements. The problem becomes even more… (More)

- Ran Wolff, Kanishka Bhaduri, Hillol Kargupta
- IEEE Transactions on Knowledge and Data…
- 2009

In a large network of computers or wireless sensors, each of the components (henceforth, peers) has some data about the global state of the system. Much of the system's functionality such as message routing, information retrieval and load sharing relies on modeling the global state. We refer to the outcome of the function (e.g., the load experienced by each… (More)

- Bobi Gilburd, Assaf Schuster, Ran Wolff
- KDD
- 2004

Secure multiparty computation allows parties to jointly compute a function of their private inputs without revealing anything but the output. Theoretical results [2] provide a general construction of such protocols for any function. Protocols obtained in this way are, however, inefficient, and thus, practically speaking, useless when a large number of… (More)