As a promising technology, key-aggregation searchable encryption with constant computation overhead is especially suitable for sensor nodes with limited computation resources in wireless sensor networks. However, in most of the existing key-aggregation searchable encryption schemes, the authorized aggregation key is generated in a deterministic way. As a result, these schemes suffer from “Key Forge Attack” and “Trapdoor Forge Attack” that we proposed and hence fail to support the security property as they claimed (which is an important goal to be achieved in key-aggregation searchable encryption schemes). To fix these flaws, in this paper, we identify the security challenges related to key-aggregation searchable encryption and propose a lightweight key-aggregation authorized searchable encryption scheme based on attribute-based encryption, called KA$^{2}$SE. It enables a data owner to share encrypted data with an authorized query user by issuing only a single authorized aggregation key, and the authorized query user only needs to submit a single trapdoor to the cloud server to perform keyword search. We formulate the security definitions for KA$^{2}$SE and prove its security. Finally, empirical evaluations demonstrate that KA$^{2}$SE is computationally efficient in comparison with existing schemes.

Convolutional neural networks (CNNs) have been successfully applied to various fields. However, CNNs' overparameterization requires more memory and training time, making it unsuitable for some resource-constrained devices. To address this issue, filter pruning as one of the most efficient ways was proposed. In this article, we propose a feature-discrimination-based filter importance criterion, uniform response criterion (URC), as a key component of filter pruning. It converts the maximum activation responses into probabilities and then measures the importance of the filter through the distribution of these probabilities over classes. However, applying URC directly to global threshold pruning may cause some problems. The first problem is that some layers will be completely pruned under global pruning settings. The second problem is that global threshold pruning neglects that filters in different layers have different importance. To address these issues, we propose hierarchical threshold pruning (HTP) with URC. It performs a pruning step limited in a relatively redundant layer rather than comparing the filters' importance across all layers, which can avoid some important filters being pruned. The effectiveness of our method benefits from three techniques: 1) measuring filter importance by URC; 2) normalizing filter scores; and 3) conducting prune in relatively redundant layers. Extensive experiments on CIFAR-10/100 and ImageNet show that our method achieves the state-of-the-art performance on multiple benchmarks.

The cloud-aided sharing of e-healthcare data has great positive significance for research. However, due to the privacy consideration, these data are usually encrypted before uploading to the cloud server which impedes data sharing between different medical institutions. Conditional proxy re-encryption (CPRE) allows the proxy to converse ciphertext, especially by specifying a condition embed in the re-encryption key to achieve fine-grained access control over the ciphertext. Unfortunately, existing CPRE schemes cannot ensure the privacy of the condition, which may contain some sensitive private information. Furthermore, a malicious proxy server may return part of the results and even false data to save its computation or bandwidth. To solve these problems, we propose a blockchain-based condition invisible proxy re-encryption scheme for the e-healthcare system. The proposed scheme guarantees the confidentiality of the data by hiding the condition in the re-encryption key so that the proxy cannot learn anything about the condition. Moreover, the ciphertext-searching algorithm is leveraged by executing the smart contract in the blockchain which ensures the results are correct and complete. Finally, experiment results demonstrate the practicability of the proposed scheme in applications.

E-Healthcare systems are increasingly popular due to the introduction of wearable healthcare devices and sensors. Personal health records (PHRs) are collected by these devices and stored in a remote cloud. Due to privacy concern, these records should not be accessible by any unauthorized party, and the cloud providers should not be able to learn any information from the stored records. To address the above issues, one promising solution is to employ attribute based encryption (ABE) for fine-grained access control and searchable encryption for keyword search on encrypted data. However, most of existing ABE schemes leak the privacy of access policy which may also contain sensitive information. On the other hand, for users' devices with limited computing power and bandwidth, the mechanism should enable them to be able to search the PHRs efficiently. Unfortunately, most existing works on ABE do not support efficient keyword search on encrypted data. In this work, we propose an efficient hidden policy ABE scheme with keyword search. Our scheme enables efficient keyword search with constant computational overhead and constant storage overhead. Moreover, we enhance the recipient's privacy which hides the access policy. As of independent interest, we present a trapdoor malleability attack and demonstrate that some of previous schemes may suffer from such attack.

Attribute-based encryption with keyword search (ABKS) is a system that supports keyword search and access control. By inheriting from attribute-based encryption technology, most current ABKS schemes incur large computation costs in encryption phase and keyword search phase. In a typical implementation, the size of ciphertext is proportional to the number of attributes associated with the access policy, so that the keyword search time and the decryption time are proportional to the number of attributes used in the access policy. To deal with the above problem, we present a new ciphertext-policy attribute-based encryption with fast keyword search scheme. Our scheme preserves the fine-grained access control inherited from the ABE system while supporting hidden policy and fast keyword search. In particular, the proposed scheme can efficiently support AND-gate access policy with multiple attribute values. Moreover, our scheme features multi-value-independent which have constant computation costs compared with the existing ABKS schemes. With the “aggregation” technique, the keyword search phase only needs three pairings, which is a great advantage over previous ABKS schemes. We offer rigorous security proof of our scheme, and the performance analysis demonstrates the efficiency of our scheme.