Bonsai Merkle Forests: Efficiently Achieving Crash Consistency in Secure Persistent Memory

Freij, Alexander; Zhou, Huiyang; Solihin, Yan

doi:10.1145/3466752.3480067

Cited by 15 publications

(3 citation statements)

References 52 publications

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“…Several studies proposed to maintain crash consistency and improve the performance of encryption in PM [8,9,16,53,93,94,99]. Some work proposed faster Merkle tree mechanisms to verify the integrity of PM [10,23,24,24,27,97,98]. Another branch of work reduces the exposure window of PM to reduce the attack surface of PM corruptions [88][89][90], even more so as cross-process attacks are feasible [58].…”

Section: Related Workmentioning

confidence: 99%

FFCCD

Solihin

et al. 2022

Proceedings of the 49th Annual International Symposium on Computer Architecture

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Persistent Memory (PM) is increasingly supplementing or substituting DRAM as main memory. Prior work have focused on reusability and memory leaks of persistent memory but have not addressed a problem amplified by persistence, persistent memory fragmentation, which refers to the continuous worsening of fragmentation of persistent memory throughout its usage. This paper reveals the challenges and proposes the first systematic crash-consistent solution, Fence-Free Crash-consistent Concurrent Defragmentation (FFCCD). FFCCD resues persistent pointer format, root nodes and typed allocation provided by persistent memory programming model to enable concurrent defragmentation on PM. FFCCD introduces architecture support for concurrent defragmentation that enables a fence-free design and fast read barrier, reducing two major overheads of defragmenting persistent memory. The techniques is effective (28-73% fragmentation reduction) and fast (4.1% execution time overhead). CCS CONCEPTS• Hardware → Non-volatile memory; • Software and its engineering → Garbage collection.

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Section: Related Workmentioning

confidence: 99%

FFCCD

Solihin

et al. 2022

Proceedings of the 49th Annual International Symposium on Computer Architecture

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“…In order to ensure the security of the main memory, integrity and confidentiality must be satisfied [9][10][11][12]. Confidentiality is achieved by encrypting data going from the processor to the external untrusted area.…”

Section: Introductionmentioning

confidence: 99%

“…Integrity ensures that the main memory correctly returns the last written data block to any address. An integrity tree is used to satisfy integrity and prevent replay attacks [9][10][11][12]. In counter-mode encryption, counters are managed by an integrity tree, where a memory write increases the corresponding counter.…”

Section: Introductionmentioning

confidence: 99%

Efficient Integrity-Tree Structure for Convolutional Neural Networks through Frequent Counter Overflow Prevention in Secure Memories

Kim

Lee

Hong

et al. 2022

Sensors

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Advancements in convolutional neural network (CNN) have resulted in remarkable success in various computing fields. However, the need to protect data against external security attacks has become increasingly important because inference process in CNNs exploit sensitive data. Secure Memory is a hardware-based protection technique that can protect the sensitive data of CNNs. However, naively applying secure memory to a CNN application causes significant performance and energy overhead. Furthermore, ensuring secure memory becomes more difficult in environments that require area efficiency and low-power execution, such as the Internet of Things (IoT). In this paper, we investigated memory access patterns for CNN workloads and analyzed their effects on secure memory performance. According to our observations, most CNN workloads intensively write to narrow memory regions, which can cause a considerable number of counter overflows. On average, 87.6% of total writes occur in 6.8% of the allocated memory space; in the extreme case, 93.9% of total writes occur in 1.4% of the allocated memory space. Based on our observations, we propose an efficient integrity-tree structure called Countermark-tree that is suitable for CNN workloads. The proposed technique reduces overall energy consumption by 48%, shows a performance improvement of 11.2% compared to VAULT-128, and requires a similar integrity-tree size to VAULT-64, a state-of-the-art technique.

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