With the development of cloud computing, patients can obtain efficient and high-quality medical services by uploading their eHealth data to the cloud for sharing among medical personnel. Because eHealth data contain lots of sensitive information, they are always encrypted before uploading to protect patients’ privacy. Ciphertext-policy attribute-based encryption (CP-ABE) is a commonly used cryptographic primitive since it achieves fine-grained and one-to-many access control on the encrypted data. However, encrypted eHealth data may become an obstacle for some healthcare scenarios, especially the emergency rescue scenes. In addition, the one-to-many access manner makes the traditional CP-ABE mechanism hard to track the identity of a traitor who sells the decryption privilege to others. In this paper, we propose an Emergency Access Control and Traceable (EmACT) attribute-based encryption scheme to address these issues. In addition, EmACT outsources the heavy bilinear computations of the decryption to the cloud, which means that EmACT is adaptable to the resource-constrained health-monitoring devices. The proposed scheme’s security is formally proven, and the experimental results demonstrate that EmACT is efficient and practicable.

Recently, the topic of learning concept prerequisite relations has gained the attention of many researchers, which is crucial in the learning process for a learner to decide an optimal study order. However, the existing work still ignores three key factors. (1) People's cognitive differences could make a difference for annotating the prerequisite relation between resources (e.g., courses, textbooks) or concepts (e.g., binary tree). (2) The current vertex (resources or concepts) can be affected by the feature of the neighbor vertex in the resource or concept graph. (3) The feature information of the resource graph may affect the concept graph. To integrate the above factors, we propose an end-to-end graph network-based model called Multi-Head Attention Variational Graph Auto-Encoders (MHAVGAE ) to learn the prerequisite relation between concepts via a resource-concept graph. To address the first two problems, we introduce the multi-head attention mechanism to operate and compute the hidden representations of each vertex over the resource-concept graph. Then, we design a gated fusion mechanism to integrate the feature information of the resource and concept graphs to enrich concept content features. Finally, we conduct numerous experiments to demonstrate the effectiveness of the MHAVGAE across multiple widely used metrics compared with the state-of-the-art methods. The experimental results show that the performance of the MHAVGAE almost outperforms all the baseline methods.

Personalized medicine performs diagnoses and treatments according to the DNA information of the patients. The new paradigm will change the health care model in the future. A doctor will perform the DNA sequence matching instead of the regular clinical laboratory tests to diagnose and medicate the diseases. Additionally, with the help of the affordable personal genomics services such as 23andMe, personalized medicine will be applied to a great population. Cloud computing will be the perfect computing model as the volume of the DNA data and the computation over it are often immense. However, due to the sensitivity, the DNA data should be encrypted before being outsourced into the cloud. In this paper, we start from a practical system model of the personalize medicine and present a solution for the secure DNA sequence matching problem in cloud computing. Comparing with the existing solutions, our scheme protects the DNA data privacy as well as the search pattern to provide a better privacy guarantee. We have proved that our scheme is secure under the well-defined cryptographic assumption, i.e., the sub-group decision assumption over a bilinear group. Unlike the existing interactive schemes, our scheme requires only one round of communication, which is critical in practical application scenarios. We also carry out a simulation study using the real-world DNA data to evaluate the performance of our scheme. The simulation results show that the computation overhead for real world problems is practical, and the communication cost is small. Furthermore, our scheme is not limited to the genome matching problem but it applies to general privacy preserving pattern matching problems which is widely used in real world.

Among all types of computations, the polynomial function evaluation is a fundamental, yet an important one due to its wide usage in the engineering and scientific problems. In this paper, we investigate publicly verifiable outsourced computation for polynomial evaluation with the support of multiple data sources. Our proposed verification scheme is universally applicable to all types of polynomial computations and allows the clients to outsource new data at any time. While the existing solutions only support the verification for polynomial evaluation over a single data source, i.e., all the inputs of the polynomial function are outsourced and signed by a single entity, our solution supports polynomial evaluations over multiple different data sources, which are more common and have wider applications, e.g., to assess the city air pollution, one needs to evaluate the environmental data uploaded from the multiple environmental monitor sites. In our proposed scheme, the verification cost for the client is independent with either the input size or the polynomial size so that it scales well in practice. We formally prove the correctness and soundness of our scheme and conduct numerical analysis and evaluation study to validate its high efficiency and scalability. The experimental results show that the data contributor signing 1000 new data only takes 2.1 s, and the verification of the delegated polynomial function takes only 22 ms, which is practically efficient for the real-world applications.

With the growing awareness of data privacy, more and more cloud users choose to encrypt their sensitive data before outsourcing them to the cloud. Search over encrypted data is therefore a critical function facilitating efficient cloud data access given the high data volume that each user has to handle nowadays. Inverted index is one of the most efficient searchable index structures and has been widely adopted in plaintext search. However, securing an inverted index and its associated search schemes is not a trivial task. A major challenge exposed from the existing efforts is the difficulty to protect user's query privacy. The challenge roots on two facts: 1) the existing solutions use a deterministic trapdoor generation function for queries; and 2) once a keyword is searched, the encrypted inverted list for this keyword is revealed to the cloud server. We denote this second property in the existing solutions as one-time-only search limitation. Additionally, conjunctive multi-keyword search, which is the most common form of query nowadays, is not supported in those works. In this paper, we propose a public-key searchable encryption scheme based on the inverted index. Our scheme preserves the high search efficiency inherited from the inverted index while lifting the one-time-only search limitation of the previous solutions. Our scheme features a probabilistic trapdoor generation algorithm and protects the search pattern. In addition, our scheme supports conjunctive multi-keyword search. Compared with the existing public key based schemes that heavily rely on expensive pairing operations, our scheme is more efficient by using only multiplications and exponentiations. To meet stronger security requirements, we strengthen our scheme with an efficient oblivious transfer protocol that hides the access pattern from the cloud. The simulation results demonstrate that our scheme is suitable for practical usage with moderate overhead.

ABSTRACT As cloud computing becomes prevalent, more and more sensitive information are being centralized into the cloud. A basic methodology that may address cloud data privacy issue is to encrypt the data before outsourcing. However, this makes effective data utilization, for example, searching a very challenging task. Although some searchable encryption schemes have been proposed to allow a user to search over encrypted data, these techniques are extremely difficult to provide efficient encrypted data query with various service patterns such as nonuniform data distribution, nonuniform query workload, and attributes join query. In this paper, we research efficient privacy‐preserved data query methodologies for querying cipher‐text numeric relational data in cloud computing. To provide efficient relational data query service just as DBMS does through SQL, we propose a service‐oriented query (SOQ) algorithm that adaptively adjusts the encrypted data buckets based on sensitive data distribution and query workload. Moreover, we propose a two‐stage index to address the issue of join query between encrypted attributes that has not been well solved to our knowledge. We design experiments to evaluate performances of our schemes and algorithms, which show that our methods achieve satisfactory encrypted data query performances. Copyright © 2013 John Wiley & Sons, Ltd.