Feature selection is a critical data preprocessing stage that has been proven beneficial in data mining and machine learning applications. As most current works focus on privacy during the training and inference tasks in machine learning, implementing privacy preservation in preprocessing is a powerful complement. In this paper, we present anefficient andprivacy-preservingfeatureselection protocol (EPFS) based on secure multiparty computation (MPC). We customize a novel method called approximate fixed-point representation to reduce the bitwidth of the sample distribution probability, thereby decreasing communication overhead. We optimize the comparison protocol by reducing the high-order bits of values according to the characteristics of the datasets and design the feature score calculation protocol together with several other MPC-based sub-protocols. We also construct an efficient feature selection workflow to obtain the reduced feature matrix, which avoids the numerous calls of secure comparison and equality test protocols in loops. Experiments on several real-world datasets show that the improved comparison protocol achieves a 29%-53% improvement in runtime and a 6%-32% reduction in communication compared to the general comparison protocol. The optimized feature selection workflow exhibits an upper performance bound, achieving a 38% improvement in runtime compared to prior work. Besides, we implement secure logistic regression training based on the selection features, where the accuracy has improved by an average of 8% compared to training on raw features.

The demand for mobile terminals to participate in data services is increasingly vital. The General Data Protection Regulation (GDPR) has established several principled requirements for data services. Existing studies focusing on data service put emphasis on data privacy and accessibility. However, they face challenges in achieving data forgetability and portability on mobile devices under GDPR and lack consideration of usage control. In this article, we propose ADSS, an app-level data service scheme for mobile devices that can beavailable-but-invisibleand guarantee fine-grained usage control. ADSS addresses the challenges by executing the logic of data usage in the Trusted Execution Environment (TEE) and managing the TEE states (i.e., data usage states) in the blockchain smart contracts. It not only satisfies the requirements of GDPR, ensuring strong security and confidentiality guarantees, but also enables the functionality of “pay-per-use”. We implement a prototype of the ADSS framework based on ARM Trustzone and conduct experimental evaluations. The results demonstrate that our scheme brings high efficiency compared with other data service schemes and exhibits feasibility on mobile-grade devices.

The technique of packing multiple values into one message without losing homomorphic computation properties is the main workhorse that drives many exciting advances in applying lattice-based homomorphic encryption schemes to privacy-preserving Machine-Learning-as-a-Service (MLaaS). However, this technique does not directly work for the classic Paillier homomorphic encryption scheme, limiting the use of the Paillier scheme in the privacy-preserving MLaaS. To enrich the applications of Paillier in privacy-preserving MLaaS, we present a set of new methods for efficient linear computations over packed values under the Paillier scheme, such as vector multiplication, matrix multiplication, and convolutional calculation between ciphertexts and plaintexts. Different from the packing methods of lattice-based schemes, the Paillier packing method naturally allows higher packing capability for values in lower bit-length. This property can significantly benefit privacy-preserving MLaaS, as the values of user inputs and parameters of machine learning models are often quantized into low bits (e.g., 1-8 bits). We conduct comparisons based on different linear computation tasks, the proposed methods under the Paillier scheme clearly outperform the state-of-the-art in terms of communication and computational efficiency, especially in realistic scenarios. For example, compared to one of the recent arts CrypTFlow2 [1], the communication cost of our solution can be 21.7× smaller at best. Thanks to the reduction of communication cost, the runtime can be 2.46× faster than CrypTFlow2 at the median-country-speed of current global mobile broadband.

LAC, a Ring-LWE based scheme, has shortlisted for the second round evaluation of the National Institute of Standards and Technology Post-Quantum Cryptography (NIST-PQC) Standardization. FPGAs are widely used to design accelerators for cryptographic schemes, especially in resource-constrained scenarios, such as IoT. Sparse Polynomial Multiplication (SPM) is the most compute-intensive routine in LAC. Designing an accelerator for SPM on FPGA can significantly improve the performance of LAC. However, as far as we know, there are currently no works related to the hardware implementation of SPM for LAC. In this paper, the proposed efficient and scalable SPM accelerator fills this gap. More concretely, we firstly develop the Dual-For-Loop-Parallel (DFLP) technique to optimize the accelerator's parallel design. This technique can achieve 2x performance improvement compared with the previous works. Secondly, we design a hardware-friendly modular reduction algorithm for the modulus 251. Our method not only saves hardware resources but also improves performance. Then, we launch a detailed analysis and optimization of the pipeline design, achieving a frequency improvement of up to 34%. Finally, our design is scalable, and we can achieve various performance-area trade-offs through parameter p. Our results demonstrate that the proposed design can achieve a very considerable performance improvement with moderate hardware area costs. For example, our medium-scale architecture for LAC-128 takes only 783 LUTs, 432 FFs, 5BRAMs, and no DSP on an Artix-7 FPGA and can complete LAC's polynomial multiplication in 8512 cycles at a frequency of 202MHz.