DATE: Tuesday, August 21, 2001
TIME: Noon - 1 pm
PLACE: Wean Hall 8220

Erik Riedel

Hewlett-Packard Labs
Palo Alto, CA

A Framework for Evaluating Storage System Security


There are a variety of ways to ensure the security of data and the integrity of data transfer, depending on the set of anticipated attacks, the level of paranoia on the part of data owners, and the level of inconvenience users are willing to tolerate. Current secure storage designs fall into two categories, either encrypting data on the wire (data protected in transit), or encrypting data at clients and on the disks (data protected when stored). These systems seem very different at first glance, and currently there are no common parameters upon which they can be compared. We have developed a single framework in which both types of systems can be evaluated along the security and performance axes. In particular, we claim that when dealing with long-term stored data, the two approaches simply represent different optimization choices along the same continuum. We use traces from a time-sharing UNIX server supporting a medium-sized workgroup to quantify the costs associated with the different design choices and outline some of the challenges for creating a truly scalable and deployable secure storage system.

^ Joint work with Mahesh Kallahalla, Christos Karamanolis, and Ram Swaminathan.



Erik Riedel is a Researcher in the storage program at Hewlett-Packard Laboratories in Palo Alto, California. His main interests are in the areas of networked and distributed storage, security, and new high-level interfaces to storage systems (the current ones are quite outdated).

Before joining HP Labs, he received a doctorate in Computer Engineering from Carnegie Mellon University working with David Nagle and Garth Gibson in the Parallel Data Lab (PDL) and Christos Faloutsos in the Center for Automated Learning and Discovery (CALD). His thesis work was on Active Disks as an extension of Network-Attached Secure Disks (NASD). Over the years he has spent time looking at I/O in a number of areas, including parallel apps, data mining, database, and scientific data processing.

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