Cheon-Kim-Kim-Song (CKKS) is a Fully Homomorphic Encryption (FHE) scheme that enables computations directly on encrypted real or complex numbers, ensuring the privacy of sensitive information even in untrusted environments. However, the processing of encrypted data incurs significant computational overhead compared to plaintext computations, making CKKS impractical for wider adoption. Field Programmable Gate Arrays (FPGAs) are a promising platform to accelerate CKKS because of parallelism, scalability, flexibility, and widespread availability across cloud providers. This paper systematically surveys the key techniques and current advancements in FPGA-based accelerators for CKKS and discusses future research trends to facilitate real-world homomorphic applications.

Autonomous vehicles use deep neural networks (DNNs) to build powerful vision perception systems, which provide a theoretical foundation for automated vehicle control. Due to the inherent vulnerability of DNNs, many research works have implemented white-box attacks against automotive vision perception systems in the physical world. However, successful black-box attacks (especially decision-based) in the physical world are rarely mentioned because it is difficult to implement a physical-world adversarial attack without internal knowledge about the vision perception systems. In this paper, we propose PRAD, an end-to-end framework that transfers the existing decision-based black-box adversarial attack algorithms (as the backbone of the framework) targeting the digital domain to the physical world for the first time. Specifically,$T(\cdot)$is first introduced to simulate the real environment changes, e.g., angle, distance, slight shaking, illumination, etc. Then, and crucially, PRAD bridges the non-differentiable black-box attack and the differentiable$T(\cdot)$by the$L_1$loss function. We use the traffic sign recognition system in the vision perception system as an object to conduct comprehensive experiments, including different environmental conditions, black-box attack backbones, models, and datasets. The results demonstrate that the generated adversarial examples in the decision-based black-box setting can fool the commercial traffic sign recognition system into outputting designated misclassifications with high success rates and strong robustness in the physical world (average 90% in target attacks and nearly 100% in non-target attacks), which outperforms the state-of-the-art homogeneous attack methods.

NAND flash memory has become increasingly popular in various computing systems. Although NAND flash memory offers attractive performance, it suffers limited operable programming and erasing cycles. To improve the reliability of flash-based systems, previous works introduce machine learning models to predict flash lifetime. These works generally focus on improving prediction accuracy but present little research about the resources required for flash lifetime prediction. In application scenarios, the overheads and the frequency of lifetime predictions are important for storage systems. Excessive prediction actions would lead to unnecessary resource consumption. For building an efficient storage system, resource requirements need to be taken into consideration when designing flash lifetime prediction schemes. In this paper, we propose adaptive lifetime prediction techniques (ADLPT) that minimize redundant prediction operations by exploiting reliability variation. To explore reliability variation, we investigate the error distribution of different 3D flash chips. Based on the investigation, a prediction judgment method is presented. The method identifies the necessary prediction by detecting the variation of erase duration and raw bit errors. Furthermore, we provide a method to improve the performance of the static model. The experimental result shows that our approach can reduce about 90% of redundant predictions with over 0.8 F1-Score.

NAND flash memory has gained popularity in a wide variety of digital storage systems. Although with excellent performance, NAND flash memory suffers various reliability problems. In recent years, researchers try to predict flash failure by using machine-learning models. However, the application of machine-learning based failure prediction method faces the following problems: imbalance between robustness and portability. When applying on different flash chips, the performance of prediction model degrades with the variation of error characteristics. In order to adapt to the variation, the machine-learning model needs to be re-built to ensure performance of failure prediction. The overheads of re-building model result in challenges when adjusting prediction model to adapt to the variation of error characteristics. To overcome these challenges, we present LightWarner, an easily applicable predictor based on model-free Reinforcement learning algorithms. LightWarner learns error characteristics dynamically during flash lifetime without pre-training. We evaluate the performance of LightWarner on six types of 3D flash chips. The evaluation result shows that LightWarner achieves over 93% F1 score on different flash chips, which is about 10% higher than supervised machine learning methods. And LightWarner can adapt to the variation of error characteristics with low migration costs.

Wear-out has been a critical reliability problem in NAND flash memory. As executing repeated program and erase operations on the NAND flash chips, the number of errors increases and ultimately exceeds the ECC capability. In previous work, error characteristics of flash wear-out are observed by endurance tests on a single type of NAND flash memory. We wonder if the experimental results cover the entire error characteristics of NAND flash memory. In this paper, we tested more than 20 types of NAND flash chips with different vendors and structures and presented an overlook of test results. Through the test results, we found an unexpected error-explosion phenomenon that errors of flash blocks first increase over several cycles and then reach a high value without warning. We analyzed the features of the error-explosion and explored its influence on operation time. And we propose an error-explosion prediction scheme to find the blocks that will occur an error-explosion in the next 1000 P/E cycles. The block identifying operation is realized by the machine-learning model. The performance of six machine-learning methods is compared. The results demonstrate that the Decision Trees and Bagged Classification Trees have the best accuracy.

In Solid State Drives, flash management techniques such as wear-leveling and refresh usually assume NAND flash memories have the same endurance value. However, the actual endurance values differ from blocks to blocks. This reliability difference is introduced by process-variation during flash fabrication. In recent years, for improving flash management techniques, various works have been done on the reliability variation of 2D flash memory. As 2D NAND transmitted to 3D NAND flash, the vertical structure and multi-layer stacking changed the effect of previously known reliability problems. In this paper, we are first to characterize the process-variation effects on 3D TLC flash reliability. The characterization includes two parts: endurance variation and error feature variation. Second, we propose an adaptive error prediction scheme to mitigate the process-variation effects. This scheme uses the machine-learning model to realize the error prediction operation. We also discuss the implications of this scheme on main flash management techniques.

The privacy-preserving authentication is considered as the first line of defense against the attacks in addition to preserving the identity privacy of the vehicles in the vehicular ad hoc networks (VANETs). However, the existing authentication schemes suffer from drawbacks such as nontransparency of the trusted authorities (TAs), heavy workload to revoke certificates, and high computation overhead to authenticate identities and messages. In this paper, we propose a blockchain-based privacy-preserving authentication (BPPA) scheme for VANETs. In BPPA, all the certificates and transactions are recorded permanently and immutably in the blockchain to make the activities of the semi-TAs transparent and verifiable. However, it remains a challenge how to use such blockchain effectively for authentication in real driving scenarios (e.g., high speed or large amount of messages during congestion). With a novel data structure named the Merkle Patricia tree (MPT), we extend the conventional blockchain structure to provide a distributed authentication scheme without the revocation list. To achieve conditional privacy, we allow a vehicle to use multiple certificates. The linkability between the certificates and real identity is encrypted and stored in the blockchain and can only be revealed in case of disputes. We evaluate the validity and performance of BPPA on the Hyperledger Fabric (HLF) platform for each entity. The experimental results show that the distributed authentication can be processed by individual vehicles within 1 ms, which meets the real-time requirement and is much more efficient, in terms of the processing time and storage requirement, than existing approaches.