Published February 2018 | Version Published
Book Section - Chapter Open

Stash in a Flash

Abstract

Encryption is a useful tool to protect data confidentiality. Yet it is still challenging to hide the very presence of encrypted, secret data from a powerful adversary. This paper presents a new technique to hide data in flash by manipulating the voltage level of pseudo-randomlyselected flash cells to encode two bits (rather than one) in the cell. In this model, we have one "public" bit interpreted using an SLC-style encoding, and extract a private bit using an MLC-style encoding. The locations of cells that encode hidden data is based on a secret key known only to the hiding user. Intuitively, this technique requires that the voltage level in a cell encoding data must be (1) not statistically distinguishable from a cell only storing public data, and (2) the user must be able to reliably read the hidden data from this cell. Our key insight is that there is a wide enough variation in the range of voltage levels in a typical flash device to obscure the presence of fine-grained changes to a small fraction of the cells, and that the variation is wide enough to support reliably re-reading hidden data. We demonstrate that our hidden data and underlying voltage manipulations go undetected by support vector machine based supervised learning which performs similarly to a random guess. The error rates of our scheme are low enough that the data is recoverable months after being stored. Compared to prior work, our technique provides 24x and 50x higher encoding and decoding throughput and doubles the capacity, while being 37x more power efficient.

Additional Information

© 2018 USENIX. We thank our shepherd and the anonymous reviewers for their insightful comments on earlier drafts of the work. This research was supported by Grant 2014621 from the United States-Israel Binational Science Foundation (BSF), by Grant CNS-1526707 from the United States National Science Foundation (NSF), and VMware. This work was done in part while Porter was at Stony Brook University.

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Additional details

Identifiers

Eprint ID
85207
Resolver ID
CaltechAUTHORS:20180308-133517936

Funding

Binational Science Foundation (USA-Israel)
2014621
NSF
CNS-1526707

Dates

Created
2018-03-08
Created from EPrint's datestamp field
Updated
2019-11-22
Created from EPrint's last_modified field

Caltech Custom Metadata

Caltech groups
Parallel and Distributed Systems Group
Other Numbering System Name
PARADISE
Other Numbering System Identifier
etr-140