The Experts below are selected from a list of 30 Experts worldwide ranked by ideXlab platform

Florian Kerschbaum - One of the best experts on this subject based on the ideXlab platform.

  • EDOC - Building a Privacy-Preserving Benchmarking Enterprise System
    11th IEEE International Enterprise Distributed Object Computing Conference (EDOC 2007), 2007
    Co-Authors: Florian Kerschbaum
    Abstract:

    Benchmarking is the process of comparing one's own performance to the statistics of a group of competitors, named peer group. It is a common and important process in the business world for many important business metrics, called key performance indicators (KPI). Privacy is of the utmost importance, since these KPIs allow the inference of sensitive information. Therefore several secure multiparty computation (SMC) protocols for securely and privately computing statistics of KPIs have recently been developed. These protocols are the basic building block for a privacy-preserving benchmarking system, but in order to complete an enterprise system that offers a benchmarking service to its customers more problems need to be solved. This paper addresses two remaining problems: peer group formation and protocol orchestration. We first analyze how peer group participation impacts privacy and vice-versa. Given current network performance limitations we conclude that in order for KPIs to remain private one subscriber can participate in at most one peer group. Peer group formation is the process of forming sensible peer groups out of the set of subscribers. A sensible peer group is one that is useful for benchmarking, i.e. a group of similar companies, under the constraint that one subscriber can participate in at most one peer group. We characterize subscribers by a set of discrete criteria and therefore view the automatic peer group formation as a data clustering problem. A data clustering algorithm customized for automatic peer group formation is required to build clusters whose size does not fall below a minimum threshold. We present a high-performance modification of k-means clustering that takes the minimum cluster size as an additional parameter which might be of independent interest. In a simulation we evaluate its practical applicability to automatic peer group formation. Our final approach is the first automatic peer group formation algorithm for an enterprise benchmarking system. Polling-based protocol orchestration allows the subscribers to remain passive clients, i.e. require no inbound connection, e.g. through a Company Firewall. We show through simulation that such a polling-based orchestration can be expected to complete within one polling interval.

  • Building a Privacy-Preserving Benchmarking Enterprise System
    11th IEEE International Enterprise Distributed Object Computing Conference (EDOC 2007), 2007
    Co-Authors: Florian Kerschbaum
    Abstract:

    Benchmarking is the process of comparing one's own performance to the statistics of a group of competitors, named peer group. It is a common and important process in the business world for many important business metrics, called key performance indicators (KPI). Privacy is of the utmost importance, since these KPIs allow the inference of sensitive information. Therefore several secure multiparty computation (SMC) protocols for securely and privately computing statistics of KPIs have recently been developed. These protocols are the basic building block for a privacy-preserving benchmarking system, but in order to complete an enterprise system that offers a benchmarking service to its customers more problems need to be solved. This paper addresses two remaining problems: peer group formation and protocol orchestration. We first analyze how peer group participation impacts privacy and vice-versa. Given current network performance limitations we conclude that in order for KPIs to remain private one subscriber can participate in at most one peer group. Peer group formation is the process of forming sensible peer groups out of the set of subscribers. A sensible peer group is one that is useful for benchmarking, i.e. a group of similar companies, under the constraint that one subscriber can participate in at most one peer group. We characterize subscribers by a set of discrete criteria and therefore view the automatic peer group formation as a data clustering problem. A data clustering algorithm customized for automatic peer group formation is required to build clusters whose size does not fall below a minimum threshold. We present a high-performance modification of k-means clustering that takes the minimum cluster size as an additional parameter which might be of independent interest. In a simulation we evaluate its practical applicability to automatic peer group formation. Our final approach is the first automatic peer group formation algorithm for an enterprise benchmarking system. Polling-based protocol orchestration allows the subscribers to remain passive clients, i.e. require no inbound connection, e.g. through a Company Firewall. We show through simulation that such a polling-based orchestration can be expected to complete within one polling interval.

Ha Jin Hwang - One of the best experts on this subject based on the ideXlab platform.

Simon Wilkie - One of the best experts on this subject based on the ideXlab platform.

  • The Economics of Cloud Computing
    The Korean Economic Review, 2011
    Co-Authors: Ergin Bayrak, John P. Conley, Simon Wilkie
    Abstract:

    Cloud computing brings together several existing technologies including service oriented architecture, distributed grid computing, virtualization, and broadband networking to provide software, infrastructure, and platforms as services. Under the old IT model, companies built their own server farms designed to meet peak demand using bundled hardware and software solutions. This was time consuming, capital intensive and relatively inflexible. Under the cloud computing model, firms can rent as many virtual machines as they need at any given time, and then either design or use off-the-shelf solutions to integrate Company-wide data in order to easily distribute access to users both within and outside of the Company Firewall. This converts fixed capital costs into variable costs, prevents under and over provisioning, and allows minute by minute flexibly. Consumers are also increasingly turning to the cloud for computing service through such applications as Gmail, Pandora, and Facebook. The purpose of this paper is to discuss this new and transformative technology, survey the existing economics literature on the subject, and suggest potential directions for new research.

Jan Seruga - One of the best experts on this subject based on the ideXlab platform.

Ergin Bayrak - One of the best experts on this subject based on the ideXlab platform.

  • The Economics of Cloud Computing
    The Korean Economic Review, 2011
    Co-Authors: Ergin Bayrak, John P. Conley, Simon Wilkie
    Abstract:

    Cloud computing brings together several existing technologies including service oriented architecture, distributed grid computing, virtualization, and broadband networking to provide software, infrastructure, and platforms as services. Under the old IT model, companies built their own server farms designed to meet peak demand using bundled hardware and software solutions. This was time consuming, capital intensive and relatively inflexible. Under the cloud computing model, firms can rent as many virtual machines as they need at any given time, and then either design or use off-the-shelf solutions to integrate Company-wide data in order to easily distribute access to users both within and outside of the Company Firewall. This converts fixed capital costs into variable costs, prevents under and over provisioning, and allows minute by minute flexibly. Consumers are also increasingly turning to the cloud for computing service through such applications as Gmail, Pandora, and Facebook. The purpose of this paper is to discuss this new and transformative technology, survey the existing economics literature on the subject, and suggest potential directions for new research.