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Emerge: Self-Emerging Data Release Using Cloud Data Storage

Li, C and Palanisamy, B (2017) Emerge: Self-Emerging Data Release Using Cloud Data Storage. In: UNSPECIFIED.

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In the age of Big Data, advances in distributed technologies and cloud storage services provide highly efficient and cost-effective solutions to large scale data storage and management. Supporting self-emerging data using clouds is a challenging problem. While straight-forward centralized approaches provide a basic solution to the problem, unfortunately they are limited to a single point of trust. Supporting attack-resilient timed release of encrypted data stored in clouds requires new mechanisms for self emergence of data encryption keys that enables encrypted data to become accessible at a future point in time. Prior to the release time, the encryption key remains undiscovered and unavailable in a secure distributed system, making the private data unavailable. In this paper, we propose Emerge, a self-emerging timed data release protocol for securely hiding data encryption keys of private encrypted data in a large-scale Distributed Hash Table (DHT) network that makes the data available and accessible only at the defined release time. We develop a suite of erasure-coding-based routing path construction schemes for securely storing and routing encryption keys in DHT networks that protect an adversary from inferring the encryption key prior to the release time (release-ahead attack) or from destroying the key altogether (drop attack). Through extensive experimental evaluation, we demonstrate that the proposed schemes are resilient to both release-ahead attack and drop attack as well as to attacks that arise due to traditional churn issues in DHT networks.


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Item Type: Conference or Workshop Item (UNSPECIFIED)
Status: Published
CreatorsEmailPitt UsernameORCID
Li, Cchl205@pitt.eduCHL205
Palanisamy, BBPALAN@pitt.eduBPALAN
Date: 8 September 2017
Date Type: Publication
Journal or Publication Title: IEEE International Conference on Cloud Computing, CLOUD
Volume: 2017-J
Page Range: 26 - 33
Event Type: Conference
DOI or Unique Handle: 10.1109/cloud.2017.13
Schools and Programs: School of Information Sciences > Information Science
Refereed: Yes
ISBN: 9781538619933
ISSN: 2159-6182
Date Deposited: 14 Jul 2017 16:29
Last Modified: 18 Sep 2021 04:57


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