With recent advances in cloud computing, mobile devices are increasingly being used to record patient physiological parameters, and transfer them to a cloud-based hospital information system, for access control mediation over a variety of stakeholders. In such a cloud-based architecture, the patient must specify an access policy for a group of authorized parties towards its outsourced data. Multiauthority ciphertext-policy attribute-based encryption (CP-ABE) was provided as an innovative cloud-based access control cryptographic primitive to tackle the key escrow issue in a centralized architecture, and boost flexibility through cross-domain attributes management. Existing works, however, still have glaring drawbacks. First, they still rely on a trusted authority to generate and distribute user secret keys. Second, they do not simultaneously provide encryption, decryption, or revocation outsourcing, resulting in high processing and communication cost for both the data sender and the data receiver. Third, they do not support both user and attribute revocation, and the integrity of ciphertext downloaded from the cloud is not always verified at the user end. As a result, this paper exploits the dummy attribute technique and introduces a novel, efficient, and secure multiauthority ciphertext-policy ABE method for mediating access control over medical data, in the mobile cloud. The ciphertext access policy enforcement, partial ciphertext decryption, and both the user and attribute indirect revocation updates are safely outsourced to the cloud server in this study. Theoretical analysis demonstrates that our scheme is efficient and verifiable, and we prove that our construction is secure under the decisional bilinear Diffie-Hellman assumption.

Differentially Private Stochastic Gradient Descent (DP-SGD) is a prime method for training machine learning models with rigorous privacy guarantees. Since its birth, DP-SGD has gained popularity and has been widely adopted in both academic and industrial research. One well-known challenge when using DP-SGD is how to improve utility while maintaining privacy. To this end, recently we have seen several proposals that clip the gradients with adaptive thresholds rather than a fixed one. Although each proposal comes with some theoretical justification, the theories often rely on strong assumptions and are not compatible with each other. It is hard to know whether they are good in practice and how good they are. In this paper, we investigate adaptive clipping in DP-SGD from an empirical perspective. With extensive experiments, we were able to gain some fresh insights and proposed two new adaptive clipping strategies based on them. We cross-compared the existing methods and our new strategies experimentally. Results showed that our strategies did provide a substantial improvement in model accuracy, and outperformed the state-of-the-art adaptive clipping methods consistently.

One key factor able to boost the applications of artificial intelligence (AI) in security-sensitive domains is to leverage them responsibly, which is engaged in providing explanations for AI. To date, a plethora of explainable artificial intelligence (XAI) has been proposed to help users interpret model decisions. However, given its data-driven nature, the explanation itself is potentially susceptible to a high risk of exposing privacy. In this paper, we first show that the existing XAI is vulnerable to model extraction attacks and then present an XAI-aware dual-task model extraction attack (DTMEA). DTMEA can attack a target model with explanation services, that is, it can extract both the classification and explanation tasks of the target model. More specifically, the substitution model extracted by DTMEA is a multitask learning architecture, consisting of a sharing layer and two task-specific layers for classification and explanation. To reveal which explanation technologies are more vulnerable to expose privacy information, we conduct an empirical evaluation of four major explanation types in the benchmark data set. Experimental results show that the attack accuracy of DTMEA outperforms the predicted-only method with up to 1.25%, 1.53%, 9.25%, and 7.45% in MNIST, Fashion-MNIST, CIFAR-10, and CIFAR-100, respectively. By exposing the potential threats on explanation technologies, our research offers the insights to develop effective tools that are able to trade off security-sensitive relationships.

Over the past few years, a variety of membership inference attacks against deep learning models have emerged, raising significant privacy concerns. These attacks can easily infer whether a sample exists in the training set of the target model with little adversary knowledge, and the inference accuracy is often much higher than random guessing, which causes serious privacy leakage. To this end, defenses against membership inference attacks have attracted great interest. However, the current available defense methods such as regularization, differential privacy, and knowledge distillation are unable to balance the trade-off between privacy and utility well. In this paper, we combine knowledge distillation and generative adversarial networks to propose a novel training framework that can effectively defend against membership inference attacks, called KD-GAN. Extensive experiments show that our method implements an attack success rate of nearly 0.5 (random guesses) which can successfully defend against membership inference attacks without causing significant damage to model utility, and consistently outperforming other defense methods in the balance of privacy and utility.