Link to the University of Pittsburgh Homepage
Link to the University Library System Homepage Link to the Contact Us Form
D-Scholarship @ Pitt Banner and Links to Homepage

PADS: Privacy-preserving Auction Design forAllocating Dynamically Priced Cloud Resources

Xu, Jinlai and Palanisamy, Balaji and Tang, Yuzhe and Kumar, SD Madhu (2017) PADS: Privacy-preserving Auction Design forAllocating Dynamically Priced Cloud Resources. In: 2017 IEEE 3rd International Conference on Collaboration and Internet Computing (CIC).

This is the latest version of this item.

[img] PDF
Download (601kB)

Abstract

With the rapid growth of Cloud Computing technologies, enterprises are increasingly deploying their services in the Cloud. Dynamically priced cloud resources such as the Amazon EC2 Spot Instance provides an efficient mechanism for cloud service providers to trade resources with potential buyers using an auction mechanism. With the dynamically priced cloud resource markets, cloud consumers can buy resources at a significantly lower cost than statically priced cloud resources such as the on-demand instances in Amazon EC2. While dynamically priced cloud resources enable to maximize datacenter resource utilization and minimize cost for the consumers, unfortunately, such auction mechanisms achieve these benefits only at a cost significant of private information leakage. In an auction-based mechanism, the private information includes information on the demands of the consumers that can lead an attacker to understand the current computing requirements of the consumers and perhaps even allow the inference of the workload patterns of the consumers. In this paper, we propose PADS, a strategy-proof differentially private auction mechanism that allows cloud providers to privately trade resources with cloud consumers in such a way that individual bidding information of the cloud consumers is not exposed by the auction mechanism. We demonstrate that PADS achieves differential privacy and approximate truthfulness guarantees while maintaining good performance in terms of revenue gains and allocation efficiency. We evaluate PADS through extensive simulation experiments that demonstrate that in comparison to traditional auction mechanisms, PADS achieves relatively high revenues for cloud providers while guaranteeing the privacy of the participating consumers.

Share

Citation/Export:
Social Networking:
Share |

Details

Item Type: Conference or Workshop Item (Paper)
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Xu, Jinlaijix67@pitt.edujix67
Palanisamy, Balajibpalan@pitt.eduBPALAN
Tang, Yuzhe
Kumar, SD Madhu
Date: 2017
Date Type: Publication
Publisher: IEEE
Place of Publication: 2017 IEEE 3rd International Conference on Collaboration and Internet Computing (CIC)
Page Range: pp. 87-96
Event Title: 2017 IEEE 3rd International Conference on Collaboration and Internet Computing (CIC)
Event Type: Conference
Schools and Programs: School of Computing and Information > Information Science
Refereed: Yes
Date Deposited: 05 Jul 2018 19:25
Last Modified: 05 Jul 2018 19:26
URI: http://d-scholarship.pitt.edu/id/eprint/34729

Available Versions of this Item

Metrics

Monthly Views for the past 3 years

Plum Analytics

Actions (login required)

View Item View Item