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In this paper, we study the problem of secure ML inference against a malicious client and a semi-trusted server such that the client only learns the inference output while the server learns nothing. This problem is first formulated by Lehmkuhl et al. with a solution (MUSE, Usenix Security’21), whose performance is then substantially improved by Chandran et al.'s work (SIMC, USENIX Security’22). However, there still exists a nontrivial gap in these efforts towards practicality, giving the challenges of overhead reduction and secure inference acceleration in an all-round way. Based on this, we propose SIMC 2.0, which complies with the underlying structure of SIMC, but significantly optimizes both the linear and non-linear layers of the model. Specifically, (1) we design a new coding method for parallel homomorphic computation between matrices and vectors. (2) We reduce the size of the garbled circuit (GC) (used to calculate non-linear activation functions, e.g., ReLU) in SIMC by about two thirds. Compared with SIMC, our experiments show that SIMC 2.0 achieves a significant speedup by up to $17.4\times$17.4× for linear layer computation, and at least $1.3\times$1.3× reduction of both the computation and communication overhead in the implementation of non-linear layers under different data dimensions. Meanwhile, SIMC 2.0 demonstrates an encouraging runtime boost by $2.3\sim 4.3\times$2.3∼4.3× over SIMC on different state-of-the-art ML models.

In this article, we present VerifyML, the first secure inference framework to check the fairness degree of a given Machine learning (ML) model. VerifyML is generic and is immune to any obstruction by the malicious model holder during the verification process. We rely on secure two-party computation (2 PC) technology to implement VerifyML, and carefully customize a series of optimization methods to boost its performance for both linear and nonlinear layer execution. Specifically, (1) VerifyML allows the vast majority of overhead to be performed offline, thus meeting the low latency requirements for online inference. (2) To speed up offline preparation, we first design novel homomorphic parallel computing techniques to accelerate the authenticated Beaver's triple (including matrix-vector and convolution triples) generation procedure. It achieves up to $1.7\times$1.7× computation speedup and gains at least $10.7\times$10.7× less communication overhead compared to state-of-the-art work. (3) We also present a new cryptographic protocol to evaluate the activation functions of non-linear layers, which is $4\times$4×–$42\times$42× faster and has $> 48\times$>48× less communication than the existing 2 PC protocol against malicious parties. In fact, VerifyML even beats the state-of-the-art semi-honest ML secure inference system! We provide a formal theoretical analysis for VerifyML security and demonstrate its performance superiority on mainstream ML models including ResNet-18 and LeNet.

The cloud-assisted mobile electronic health (e-health) system facilitates e-health data sharing between healthcare providers and patients, but also raises the security and privacy concerns of e-health data. Although Ciphertext-Policy Attribute-Based Encryption (CP-ABE) has been a promising technique to achieve fine-grained access control over encrypted e-health data, it still incurs high encryption and decryption burdens on mobile users such as smartphones and sensors. In addition, malicious cloud servers may conduct incorrect operations due to various interest incentives (e.g., leaking sensitive information to illegal users, saving computation and storage costs). To solve the above issues, in this paper we first propose an Outsourced CP-ABE (OABE) with verifiable encryption scheme by splitting secret keys corresponding to an attribute set and using the short signature, which not only reduces the encryption and decryption complexities of mobile users but also guarantees that cloud servers correctly perform encryption operations. Then, we extend OABE to construct outsourced CP-ABE with verifiable decryption (OABE+) by utilizing the verifiable tag mechanism, which guarantees that cloud servers correctly conduct the ciphertext transformation. Formal security analysis proves that our schemes are selectively secure against unauthorized accesses and malicious operations. Extensive experiments using various real-world datasets demonstrate that our schemes are efficient and feasible in real applications.

Electronic health (e-health) systems may outsource data such as patient e-health records to mobile cloud servers for efficiency gains (e.g., minimizing local storage and computation costs). However, such a move may result in privacy implications in the presence of semi-honest cloud servers. Searchable Encryption (SE) can potentially facilitate privacy-preserving searches based on keywords for encrypted data stored in the mobile cloud, but most existing SE solutions do not support temporal access control (i.e., a mechanism that grants access permissions to users for specified time ranges). Hence, in this paper we design a time-controllable keyword search scheme by using an attribute-based comparable access control. This allows users to match indexes encrypted at specified time intervals. Then, we improve the basic framework to support efficient user revocation using secret sharing. We then formally prove the security of our proposed frameworks against chosen-keyword attack and key collusion attack, as well as achieving keyword secrecy. We also evaluate the performance of our proposed approach using a real-world dataset to demonstrate their practical utility.

The rapid development and wide application of blockchain not only highlight the significance of privacy protection (including anonymity and confidentiality) but also the necessity of auditability. While several ingenious schemes such as MiniLedger and traceable Monero supporting both privacy protection and auditability have been proposed, they either provide incomplete privacy protection (only achieving anonymity within a small set or only providing confidentiality but not anonymity), or involve additional auditing conditions such as reaching threshold transaction volume or requiring permissioned nodes to serve as the manager, or restrict to specific blockchain types such as Monero. To mitigate these issues, this article proposes a generic anonymous, confidential, and auditable transaction system (named ACA), which is compatible with both UTXO-based permissionless and permissioned blockchains. Core technologies of ACA include designed traceable anonymous key generation and publicly verifiable authorization mechanisms from existing cryptographic tools (i.e., public key encryption, partially homomorphic encryption, and accumulator) as well as the meticulous designed signatures of knowledge and smart contract. To demonstrate the entity of our proposal, we first prove its security including authenticity, anonymity, confidentiality and soundness, and then provide an instantiation to evaluate its performance. The final implementation and benchmarks show that our proposal can still gain performance advantage even adding more functionalities.