In the business scenarios at Ant Group, there is a rising demand for collaborative data analysis among multiple institutions, which can promote health insurance, financial services, risk control, and others. However, the increasing concern about privacy issues has led to data silos. Secure Multi-Party Computation (MPC) provides an effective solution for collaborative data analysis, which can utilize data value while ensuring data security. Nevertheless, the performance bottlenecks of MPC and the strong demand for scalability pose great challenges to secure collaborative data analysis frameworks. In this paper, we build a secure collaborative data analysis system SCQL with a general purpose. We design more efficient MPC protocols and relational operators to meet the demand for scalability. In terms of system design, we aim to implement a system with security, usability, and efficiency. We conduct extensive experiments on SCQL to validate our optimization improvements: (1) Our optimized secure sort protocol sorts one million 64-bit data in only 4.5 minutes, 126× faster than EMP (9.4 hours). (2) The end-to-end execution time of the typical vertical scenario query is reduced by 1991× from the state-of-the-art semi-honest collaborative analysis framework Secrecy (rewritten with Additive Secret Sharing protocol), with appropriate security tradeoffs. (3) We test the system in the WAN setting with input size = 10 7 to demonstrate the scalability. We have successfully deployed SCQL to address problems in real-world business scenarios at Ant Group.

Convolutional Neural Networks (CNNs) possess extensive applicability across diverse domains, particularly in the realm of image recognition. In light of the advent of machine learning as a service, the utilization of a well-trained CNN model by servers to execute image classification based on user queries has become a significant service, catering to a wide array of applications. Nevertheless, this convenience is accompanied by the inherent risk of data privacy and model privacy disclosure, which can have severe ramifications, particularly in the context of specialized scenarios like medical images and location images. Hence, how to perform classification for CNN with privacy protection emerges as a crucial research concern. Furthermore, the nonlinearity of CNN's activation function renders it unsuitable for homomorphic cryptosystems. In order to address these challenges, we put forth a privacy-preserving CNN classification scheme employing a distributed two trapdoors public-key cryptosystem (DT-PKC). Initially, we introduce a security protocol toolkit encompassing protocols for secure multiplication, secure activation function computing, and average pooling. In addition, we propose a novel continuous and derivative Tanhplus function as an approximation of the Relu function, aiming to enhance the accuracy of classification results. The secure activation function computing protocol utilizes the aforementioned Tanhplus function in conjunction with the proposed homogenization algorithm to compute the activation function. This protocol guarantees more precise and accurate output in the activation function calculation of CNN when operating under ciphertext. Furthermore, the adoption of the DT-PKC cryptosystem not only ensures privacy protection for CNN classification but also provides support for lightweight users and multiple keys. Finally, security analysis and performance evaluations demonstrate that the proposed scheme is secure, practicable, and efficient with high accuracy.

Abstract Fully homomorphic encryption (FHE) allows direct computations over the encrypted data without access to the decryption. Hence multi-key FHE is well suitable for secure multiparty computation. Recently, Brakerski et al. (TCC 2019 and EUROCRYPT 2020) utilized additively homomorphic encryption to construct FHE schemes with different properties. Motivated by their work, we are attempting to construct multi-key FHE schemes via additively homomorphic encryption. In this paper, we propose a general framework of constructing multi-key FHE, combining the additively homomorphic encryption with specific multiparty computation protocols constructed from encryption switching protocol. Concretely, every involved party encrypts his plaintexts with an additively homomorphic encryption under his own public key. Then the ciphertexts are evaluated by suitable multiparty computation protocols performed by two cooperative servers without collusion. Furthermore, an instantiation with an ElGamal variant scheme is presented. Performance comparisons show that our multi-key FHE from additively homomorphic encryption is more efficient and practical.

Abstract Multi-key fully homomorphic encryption (MFHE) supports arbitrary meaningful computations on encrypted data under different public keys even without access to the secret key, which is well tailored for the secure multiparty computation scenarios. Based on the Gentry–Sahai–Waters scheme (a single-key FHE in Crypto 2013) with the underlying learning with errors problem, MW16 scheme (Eurocrypt 2016) utilizes the method of ‘linear combination procedure’ (LCP) as a subroutine to construct the auxiliary information for the expanded ciphertexts of MFHE scheme. However, every party shares a common random string (CRS) to be distributed by a trusted setup, which is unpractical. Meanwhile, the noise in the auxiliary information is too much compared with the one in fresh ciphertexts. In this paper, we propose a modified MFHE scheme in the plain model, i.e. without CRS, to enhance the practicability of MFHE. Specifically, every involved party generates his own public key independent on a CRS. Then a potential improvement on the LCP is developed to provide auxiliary information, which largely reduces the noise and leads to a smaller modulus for our MFHE. Furthermore, the feasibility of our proposal is also proved by theoretical performance comparisons.

Convolutional neural networks (CNNs) have excellent and extensive applications in image recognition. With the continuous exploitation of data value and the proliferation of machine learning-as-a-service, convolutional neural network prediction schemes on privacy preservation have been introduced one after another, which makes much more attention focused on the privacy leakage and services offered to be efficient and light. Therefore, how to improve the convolutional neural prediction scheme on the premise of privacy preservation turns out to be an imperative research issue. In this paper, we propose a privacy-preserving convolutional neural network prediction scheme (PCP-LL) that supports low latency and lightweight users. The scheme starts from the perspective of lossless accuracy from underlying networks. First, we construct a secure activation function computing protocol (SActF) utilizing a commodity-based secure comparison protocol, which reduces the complexity and latency during the activation function computing under ciphertexts compared with common schemes. Second, to further support lightweight users, we introduce a secure output layer protocol (SOut) that enables users to obtain the prediction results without extra decryption after simple operations. Then, the scheme adopts the distributed two trapdoors public-key cryptosystem (DT-PKC) to achieve both data and model security, which well avoids security issues especially such as wiretapping by semi-honest participants commonly in secret sharing schemes. Finally, through relevant evaluations, the scheme not only achieves privacy preservation and low latency, but also supports lightweight users.

Industrial Internet of Things (IIoT) is gradually changing the mode of traditional industries with the rapid development of big data. Besides, thanks to the development of deep learning, it can be used to extract useful knowledge from the large amount of data in the IIoT to help improve production and service quality. However, the lack of large-scale data sets will lead to low performance and overfitting of learning models. Therefore, federated deep learning with distributed data sets has been proposed. Nevertheless, the research has shown that federated learning can also leak the private data of participants. In IIoT, once the privacy of participants in some special application scenarios is leaked, it will directly affect national security and people’s lives, such as smart power grid and smart medical care. At present, several privacy-preserving federated learning schemes have been proposed to preserve data privacy of participants, but security issues prevent them from being fully applied. In this article, we analyze the security of the DeepPAR scheme proposed by Zhang et al., and point out that the scheme is insecure in the re-encryption key generation process, which will cause the leakage of the secret key of participants or the proxy server. In addition, the scheme is not resistant to collusion attacks between the parameter server and participants. Based on this, we propose an improved scheme. The security proof shows that the improved scheme solves the security problem of the original scheme and is resistant to collusion attacks. Finally, the security and accuracy of the scheme is illustrated by performance analysis.

With the rapid development of machine learning in the medical cloud system, cloud-assisted medical computing provides a concrete platform for remote rapid medical diagnosis services. Support vector machine (SVM), as one of the important algorithms of machine learning, has been widely used in the field of medical diagnosis for its high classification accuracy and efficiency. In some existing schemes, healthcare providers train diagnostic models with SVM algorithms and provide online diagnostic services to doctors. Doctors send the patient's case report to the diagnostic models to obtain the results and assist in clinical diagnosis. However, case report involves patients' privacy, and patients do not want their sensitive information to be leaked. Therefore, the protection of patient's privacy has become an important research direction in the field of online medical diagnosis. In this paper, we propose a privacy-preserving medical diagnosis scheme based on multi-class SVMs. The scheme is based on the distributed two trapdoors public key cryptosystem (DT-PKC) and Boneh-Goh-Nissim (BGN) cryptosystem. We design a secure computing protocol to compute the core process of the SVM classification algorithm. Our scheme can deal with both linearly separable data and nonlinear data while protecting the privacy of user data and support vectors. The results show that our scheme is secure, reliable, scalable with high accuracy.

The collection of users’ near-real-time electricity consumption data brings advantages to the operation of smart grids, while raising some security and privacy issues. Multiple privacy preserving data aggregation schemes have been proposed to address these problems. However, most schemes only focus on the aggregation of electricity consumption data without considering the data availability. In addition, although a data aggregation scheme that supports function queries on encrypted data has also been developed, its efficiency is insufficient. In this paper, we first propose an EC-ElGamal encryption algorithm with a double trapdoor decryption mechanism. Through employing the proposed algorithm and the elliptic curve Schnorr signature scheme, an efficient data aggregation scheme supporting privacy protection and function query is proposed for smart grids. This solution allows the control center and users to initiate various function queries on encrypted data. In order to lighten the calculation burden of the control center, we propose another cryptosystem named ElGamal-OU to improve the decryption efficiency, which also supports two independent decryption methods. Finally, the security analysis and performance comparison with related work show that our schemes have advantages in terms of computational, communication and storage overhead.

Variable impedance control is advantageous for physical human-robot interaction to improve safety, adaptability and many other aspects. This paper presents a gain-scheduled variable stiffness control approach under strict frequency-domain constraints. Firstly, to reduce conservativeness, we characterize and constrain the impedance rendering, actuator saturation, disturbance/noise rejection and passivity requirements into their specific frequency bands. This relaxation makes sense because of the restricted frequency properties of the interactive robots. Secondly, a gain-scheduled method is taken to regulate the controller gains with respect to the desired stiffness. Thirdly, the scheduling function is parameterized via a nonsmooth optimization method. Finally, the proposed approach is validated by simulations, experiments and comparisons with a gain-fixed passivity-based PID method.