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As a useful cryptographic primitive, searchable symmetric encryption (SSE) has been intensively studied to achieve the secure and efficient retrieval of encrypted data. In order to process update operations, dynamic SSE schemes have been proposed. But recently, file-injection attack has threatened the security of traditional dynamic SSE protocols. Therefore, designing dynamic SSE schemes with forward privacy becomes a new demand to resist the above attack. Meanwhile, multi-client setting is another requirement in SSE techniques where multiple clients can be delegated and have access to the database. However, most of previous forward private schemes were constructed for single-client setting and cannot directly extended to multi-client environment efficiently. To solve the problem, we propose a forward private SSE scheme with support for multi-client in cloud computing. The proposed scheme is based on XOR-homomorphic function and involves two new data structures as private link and public search tree. Security proof demonstrates the proposed scheme can meet the desired secure features. We then conduct experimental evaluation of the proposed scheme and make comparison with related schemes. The result shows that the proposed scheme tends to have high efficiency.

The smart grid has attracted great attention as a new energy network. The power supplier collects the total consumption data through the smart meter. The data aggregation is a common operation in smart grid. However, the majority of existing data aggregation schemes are based on homomorphic encryption, suffering from heavy computation overhead. Recently, Gope et al. put forward a masking-based data aggregation scheme in smart grid, but there are some security problems by analyzing their scheme. If one of consumption data does not arrive at the aggregator on time, the power supplier will get incorrect aggregation results. To address these issues, we propose a lightweight and fault-tolerable data aggregation scheme. It can determine which smart meters fail to upload data on time with the idea of flag bit, and then power supplier can calculate the masking value of failed smart meter. So the proposed scheme can ensure the correctness of the final aggregation result even if a smart meter fails to upload data. In addition, the proposed scheme can achieve authentication and data collection only using one round of communication interaction while other schemes need more rounds. Security proof shows that the proposed scheme can achieve indistinguishable privacy. Experimental data confirms the efficiency of the proposed scheme.

The notion of Remote Data Auditing (RDA) has been put forward to achieve the integrity verification for cloud data. However, most of the existing RDA techniques suffer from heavy computational overhead or security attacks. To address this challenge, we introduce an online/offline remote data auditing (OORDA) framework that defines the data auditing process as online and offline phases. Then a concrete OORDA scheme is proposed to ensure secure integrity checking for cloud data. Since some expensive computations are performed offline in advance, the online computational cost can be greatly reduced, which can well solve the performance bottleneck for auditing large-scale data on the auditor. Based on the Computational Diffie–Hellman problem, the proposed OORDA scheme is provably secure in the random oracle model. Performance analysis confirms that the proposed scheme has the optimized efficiency compared with existing schemes, where the average online computational cost can be improved by more than 68% during auditing. Subsequently, a Batch OORDA scheme is presented to support batch data auditing operations, which can reduce the number of pairing operations to constant size at the online phase, independent of the number of data owners. Furthermore, we discuss that the designed file block tag and auditing process can be applied to other related fields, such as verifiable keyword search, verifiable SQL query.

Elaiophylin (Ela), a unique 16-membered symmetric macrodiolide antibiotic, displays broad biological activity. Two rare 2-deoxy-L-fucose moieties at the ends of Ela are critical for its activity. Previously, elaiophylin glycosyltransferase (ElaGT) was identified as the enzyme that is responsible for the symmetric glycosylation of Ela, acting as a potential enzymatic tool for enhancing the diversity and activity of Ela. However, a symmetric catalytic mechanism has never been reported for a glycosyltransferase (GT). To explore the catalytic mechanism, the structure of ElaGT was determined in four forms: the apo form and Ela-bound, thymidine diphosphate-bound and uridine diphosphate-bound forms. In the Ela-bound structure, two ElaGTs form a `face-to-face' C2-symmetric homodimer with a continuous acceptor-binding pocket, allowing a molecule of Ela to shuffle through. Interestingly, this dimer interface resembles that of the activator-dependent GT EryCIII with its activator EryCII. Sequence analysis also indicates that ElaGT belongs to the activator-dependent GT family, but no putative activator has been identified in the Ela gene cluster. It was then found that the ElaGT homodimer may utilize this `face-to-face' arrangement to stabilize the Ela-binding loops on the interface and to simultaneously allosterically regulate the catalytic center. Therefore, these structures present a novel self-activating model for symmetric sugar transfer in the GT family and a new potential regulation site for substrate specificity.

In the research of searchable encryption, fine-grained data authorization is a convenient way to manage the search rights for users. Recently, Liu et al. proposed a fine-grained searchable scheme with verification, which can control the search authorization and verify the results. In this paper, we first present a forgery attack against Liu et al.’s scheme and then propose a novel scheme of verifiable data search with fine-grained authorization in edge environment. Based on the key aggregate mechanism and Merkle hash tree, our proposed scheme not only achieves file-oriented search permission management but also implements the correctness and completeness verification of search results. In addition, with the assistance of edge server, resource-constrained users can easily perform the tasks of search and verification. Finally, we prove our scheme is secure based on the decision l -bilinear Diffie–Hellman exponent problem. The performance analysis and experiment results demonstrate that our proposed scheme has lower computation, communication, and storage costs contrast to the existing schemes.

Phosphatidylinositol 3,5-bisphosphate (PI(3,5)P2) is an essential phosphoinositide required for endosome homeostasis and sorting for lysosomal degradation; however, the underlying mechanisms, especially in mammals, remain elusive or unexplored. Here we determined a structure of PI(3,5)P2 bound to Sorting Nexin 11 (SNX11) with an opened PPII-C loop. We also obtained an SNX11 structure with its PPII-C in "closed" form that serves as a potential PI3P-binding model. In addition, our results reveal that SNX11 can interact with the V1D subunit of vacuolar H+-ATPase (V-ATPase), which provides a link between PI(3,5)P2 and human V-ATPase and further evidence for their roles in the endosome homeostasis regulation. Lastly, a new apo-form structure of SNX11, combined with molecular dynamics (MD) studies, indicates that the α5 helix can unfold from the PX domain of SNX11 when targeting the membrane or interacting with its partner. Taken together, these findings identify a novel PI(3,5)P2 effector, which will shed light on the PIs recognizing mechanism and the understanding of the downstream sorting events triggered by different PI binding.

With the rapid development of the Internet of Things, the massive amount of big data generated by the Internet of Things terminals and the real-time processing requirements have brought enormous challenges. A two-tier computing model consisting solely of two entities, cloud and user, will not be sufficient to support processing large numbers of concurrent data requests. Therefore, fog computing was proposed. How to realize the secure and efficient deduplication of ciphertext in fog computing has become a new research topic. In this paper, we firstly present a new decentralized deduplication structure and then show how to apply it to construct a secure and efficient big data deduplication scheme in fog computing. The cloud server, in the proposed paper, can quickly determine which fog server needs to be traversed to search duplicate data, and instead of traversing all fog servers. This significantly improves the efficiency of big data deduplication in fog computing. Furthermore, the proposed scheme allows fog server to verify whether the user possesses the ownership of the data. Performance analysis and experimental results show the proposed scheme has less overheads than existing schemes.

Searchable encryption (SE) allows client to outsource personal data to an untrusted server while protecting the data privacy, which is widely used by corporations and individuals. Recently, many works show that forward privacy is the fundamental property for secure SE cryptosystem, and several forward secure SE schemes have been proposed. However, most forward secure schemes are focus on the single-keyword query, which limit their wide application in cloud computing. In this paper, we propose an efficient forward secure searchable encryption scheme supporting multi-keyword query. Our scheme involves two new storage structures as temporary map and search tree. Specifically, when the client uploads multiple files with the same keyword, our scheme uses an encryption key to replace multiple encryption keys in the existing schemes, so that improves query efficiency. Moreover, our scheme overcomes the shortcoming that the query complexity increases linearly with the number of updated files in the existing schemes. We prove our scheme is secure with forward privacy under the random oracle model. The experimental results show that our scheme is more efficient than the existing schemes.

Phosphatidylinositol 3,5-bisphosphate (PI(3,5)P

The growing popularity of cloud-based deep learning raises a problem about accurate prediction and data privacy. Previous studies have implemented privacy prediction for simple neural networks. Since more complex neural networks require more computational overhead, existing privacy prediction schemes are inefficient. To tackles the above problem, this paper introduces a privacy prediction method for lightweight convolutional neural network (CNN) that can be applied to encrypted data. Firstly, the complex CNN is pruned into a lightweight network without compromising the original accuracy, which can realize secure prediction efficiently. Secondly, the FV homomorphic encryption scheme is adopted to encrypt the user’s sensitive data and each layer in CNN is calculated on the ciphertext, so as to protect user’s data privacy. Finally, the security analysis and experiment results demonstrate the privacy-preserving property and practicability of the proposed scheme, where the complex CNN on the MNIST data set can achieve more than 98% accuracy.

For authentication schemes based on smart card in multi-server environment, there is a common problem—the parameters stored in a user’s smart card linearly increase with the number of the registered servers without involving registration center in helping with authentication process. Therefore, in order to save storage cost and raise efficiency, an authentication scheme based on smart card is proposed in multi-server environment. In the proposed scheme, a user only needs one time registration, and keeps a password to realize the authentication with different servers without involving the registration center in helping with authentication process. The server is unnecessary to store any users information to achieve authentication between the users and the server. Moreover, the security of the proposed scheme is proved by Burrows–Abadi–Needham logic. The security analysis and the performance analysis show the proposed scheme is secured against various well known attacks, and has lower communication cost, computation cost and storage cost compared with several related schemes.