This paper studies MPC based decision graph inference (MDGI) where decision graphs (generalization of decision trees) are very popular machine learning models. In MDGI, a modeler holding a private model and a data owner holding private feature vectors jointly run model inference on each vector through an MPC protocol, which outputs the inference result without revealing the model or vector. Several noteworthy MDGI solutions have been proposed, but they remain unsatisfactory for large models due to the high communication cost of oblivious decision, the most complex component in MDGI. Oblivious decision securely evaluates binary tests (Boolean-valued functions) over features without revealing test type, parameter, feature index, or value. All constant-round oblivious decision protocols suffer from high communication costs due to bitwise encryption and transmission. Moreover, most support only one of the three common test types: threshold comparison, equality test, and set containment. We propose Homomorphic Lookup Table (HLT), a novel MPC technique for oblivious decision. HLT circumvents the bitwiseencryption and heavy-communication issue by adopting tablelookup-style computation and amortizing the cost of encrypted table transfer across many inferences. We carefully design the table structure and lookup rules, and integrate homomorphic encryption to optimize performance and support multiple test types. HLT achieves 78 × −4151× reduction in communication for oblivious decision, and is the first method to support all three common test types. Based on HLT, we design constant-round MDGI solutions for two widely used decision graphs: decision trees and scorecards. This is the first privacy-preserving solution for scorecards, and our decision tree solution reduces communication by 27 × −321×.

Emerging Non-Volatile Memory (NVM) has many advantages, such as near-DRAM speed, byte-addressability, and persistence. Modern computer systems contain many memory slots, which are exposed as a unified storage interface by shared address space. Since NVM has limited write endurance, many wear-leveling techniques are implemented in hardware. However, existing hardware techniques can only effective in a single NVM slot, which cannot ensure wear-leveling among multiple NVM slots. This paper explores how to optimize a storage system with multiple NVM slots in terms of performance and lifetime. We show that simple integration of multiple NVMs in traditional memory policies results in poor reliability. We also reveal that existing hardware wear-leveling technologies are ineffective for a system with multiple NVM slots. In this paper, we propose a common wear-aware memory management scheme for in-memory file system. The proposed memory scheme enables wear-aware control of NVM slot use which minimizes the cost of performance and lifetime. We implemented the proposed memory management scheme and evaluated their effectiveness. The experiments show that the proposed wear-aware memory management scheme can outperform wear-leveling effect by more than 2600x, and the lifetime of NVM can be prolonged by 2.5x, the write performance can be improved by up to 15%.

The development of cloud computing and big data results in a large amount of data transmitting and storing. In order to protect sensitive data from leakage and unauthorized access, many cryptographic file systems are proposed to transparently encrypt file contents before storing them on storage devices, such as eCryptfs. However, the time-consuming encryption operations cause serious performance degradation. We found that compared with non-crypto file system EXT4, the performance slowdown could be up to 58.53 and 86.89 percent respectively for read and write with eCryptfs. Although prior work has proposed techniques to improve the efficiency of cryptographic file system through computation acceleration, no solution focused on the inefficiency working flow, which is demonstrated to be a major factor affecting system performance. To address this open problem, we present NV-eCryptfs, an asynchronous software stack for eCryptfs, which utilizes NVM as a fast storage tier on top of slower block devices to fully parallelize encryption and data I/O. We design an efficient NVM management scheme to support the fast parallel cryptographic operations. Besides providing an address space that can be directly accessed by the hardware accelerators, our designed mechanism is able to record the memory allocation states, and supplies a backup plan to deal with the situation of NVM shortage. The additional index structure is built to accelerate lookup operations to determine if a given data block resides in NVM. Moreover, we integrate an adaptive scheduling in NV-eCryptfs to process I/O requests dynamically according to access pattern and request size, which is able to take full utilization of both software and hardware acceleration to boost crypto performance. Our evaluation shows the proposed NV-eCryptfs outperforms the original eCryptfs with software routine 23.41× and 5.82× respectively for read and write.