ADS: Leveraging Approximate Data for Efficient Data Sanitization in SSDs

Abstract

NAND flash memory has been widely adopted in emerging storage systems. To ensure data security, the support of data sanitization in NAND flash memory-based storage systems is widely employed. Although some existing studies made efforts in employing encryption-based, erasure-based, and scrubbing-based secure deletion approaches to achieve the security requirement, they suffer from the risk of being deciphered, the severe performance, and the scrubbing disturbance problems. Meanwhile, 3-D NAND flash technology, which stacks flash cells in vertical direction, is gaining traction in the modern systems. This made the problems more severe because of the increased number of scrubbing disturbance directions in 3-D NAND flash memory. To address the above issue, this work proposes an approximate-data-aware data sanitization scheme (ADS) with the assistance of the error-resilient data of modern applications, which guarantees the highest degree of security for security-sensitive data sanitization (i.e., storage systems do not keep any old version of secure data once secure data are updated). ADS classifies request data into approx-secure (AS), precise-secure (PS), approx-unsecure (AU), and precise-unsecure (PU) data by considering two factors, including data error resilience and data privacy. Then, a novel data allocation strategy is proposed to selectively interleave secure data and approximate data within the flash blocks, which creates the scrubbing friendly data patterns to minimize the overhead of secure deletion. Our experimental results show that ADS reduces the average secure deletion latency by 58.93% over the state of the art.