Fog computing is a paradigm that extends cloud computing to the edge of the network. It can provide computation and storage services to end devices in Internet of Things (IoT). Attribute-based cryptography is a well-known technology to guarantee data confidentiality and fine-grained data access control. However, its computational cost in encryption and decryption phase is linear with the complexity of policy. In this paper, we propose a secure and fine-grained data access control scheme with ciphertext update and computation outsourcing in fog computing for IoT. The sensitive data of data owner are first encrypted using attribute-based encryption with multiple policies and then outsourced to cloud storage. Hence, the user whose attributes satisfy the access policy can decrypt the ciphertext. Based on the attribute-based signature technique, authorized user whose attributes integrated in the signature satisfy the update policy can renew the ciphertext. Specifically, most of the encryption, decryption, and signing computations are outsourced from end devices to fog nodes, and thus, the computations for data owners to encrypt, end users to decrypt, re-encrypt, and sign are irrelevant to the number of attributes in the policies. The security analysis shows that the proposed scheme is secure against known attacks, and the experimental results show that the fog nodes perform most of the computation operations of encryption, decryption, and signing, and hence, the time of encryption for data owner, decryption, re-encryption, and signing for users is small and constant.

Mobile healthcare social networks (MHSN) integrated with connected medical sensors and cloud-based health data storage provide preventive and curative health services in smart cities. The fusion of social data together with real-time health data facilitates a novel paradigm of healthcare big data analysis. However, the collaboration of healthcare and social network service providers may pose a series of security and privacy issues. In this paper, we propose a secure health and social data sharing and collaboration scheme in MHSN. To preserve the data privacy, we realize secure and fine-grained health data and social data sharing with attribute-based encryption and identity-based broadcast encryption techniques, respectively, which allows patients to share their private personal data securely. In order to achieve enhanced data collaboration, we allow the healthcare analyzers to access both the reencrypted health data and the social data with authorization from the data owner based on proxy reencryption. Specifically, most of the health data encryption and decryption computations are outsourced from resource-constrained mobile devices to a health cloud, and the decryption of the healthcare analyzer incurs a low cost. The security and performance analysis results show the security and efficiency of our scheme.

Cloud computing and social networks are changing the way of healthcare by providing realtime data sharing in a cost-effective manner. However, data security issue is one of the main obstacles to the wide application of mobile healthcare social networks (MHSNs), since health information is considered to be highly sensitive. In this paper, we introduce a secure data sharing and profile matching scheme for the MHSN in cloud computing. The patients can outsource their encrypted health records to cloud storage with an identity-based broadcast encryption technique, and share them with a group of doctors in a secure and efficient manner. We then present an attribute-based conditional data re-encryption construction which permits the doctors who satisfy the pre-defined conditions in the ciphertext to authorize the cloud platform to convert a ciphertext into a new ciphertext of an identity-based encryption scheme for specialist without leaking any sensitive information. Furthermore, we provide a profile matching mechanism in the MHSN based on identity-based encryption with an equality test, which helps patients to find friends in a privacy-preserving way and achieves flexible authorization on the encrypted health records with resisting the keywords guessing attack. Moreover, this mechanism reduces the computation cost on the patient side. The security analysis and experimental evaluation show that our scheme is practical for protecting the data security and privacy in the MHSN.

With the popularization of location-based services (LBS), encryption techniques have been utilized to protect data security when outsourcing LBS to cloud. However, existing schemes only consider spatial range search or keyword search, while expressive and practical search over encrypted LBS data is still a challenging problem. In this paper, we introduce PrivSTL, a privacy-preserving spatio-temporal keyword search framework over the encrypted LBS data based on attribute-based encryption and linear encryption. It allows mobile users to submit LBS query with spatial range, time interval and Boolean keyword expression, and provides accurate and authorized search by matching these query conditions and also the access policy. Then we introduce an extended scheme PrivSTG, which utilizes Geohash to divide the locations into grids, and outsources an encrypted index tree to cloud servers. PrivSTG improves the service efficiency by searching only over the ciphertexts in the surrounding grids of mobile user. Finally, we analyze the security of PrivSTL against chosen-plaintext, chosen-keyword and outside keyword-guessing attacks in generic bilinear group model, and show that PrivSTL guarantees the spatio-temporal keyword profile privacy, and also protects the query privacy of mobile user. The experimental results indicate that our scheme is practical and efficient for outsourced LBS.

Attribute-based keyword search (ABKS) has been proposed to realize fine-grained access control and provide search service in cloud computing. However, most ABKS schemes focus on single or conjunctive keyword search, while the recent Boolean keyword search schemes only support monotonic query formula mainly involving AND, OR and threshold operators. How to support more expressive Boolean query formulas and return the corresponding accurate search results to users have become challenges for practical ABKS over ciphertexts. In this paper, we introduce an attribute-based expressive and ranked keyword search scheme over encrypted documents named ABERKS, which allows authorized users to submit expressive Boolean query formulas involving AND, OR, NOT and threshold operators. ABERKS utilizes a non-monotonic access tree structure to construct the query formula, and further leverages extended Boolean model to rank the search results. Specifically, the users are able to define the weights in the query formula, and get the relevance score of each matched ciphertext if the attributes and keywords are both satisfied. We prove the security of ABERKS against chosen keyword attack under selective ciphertext policy model and against keyword guessing attack, and also conduct extensive experiments to show the efficiency and practicality of ABERKS.