Blockchain-based multi-authority revocable data sharing scheme in smart grid

In view of the problems of inefficient data encryption, non-support of malicious user revocation and data integrity checking in current smart grid data sharing schemes, this paper proposes a blockchain-based multi-authority revocable data sharing scheme in the smart grid. Using online/offline encryption technology with hybrid encryption technology enhances the encryption performance for the data owner. The use of user binary tree technology enables the traceability and revocability of malicious users. The introduction of multiple attribute authorization authorities eliminates the threat of collusive attacks that exist in traditional data-sharing schemes. In addition, the semi-honest problem of third-party servers is solved by uploading data verification credentials to the blockchain. The security analysis results show that the scheme can resist selective plaintext attacks and collusion attacks. The performance analysis results show that the proposed scheme has lower computational overhead and better functionality than similar schemes, which is suitable for secure data sharing in smart grids

Revocable Key-Aggregate Cryptosystem for Data Sharing in Cloud

With the rapid development of network and storage technology, cloud storage has become a new service mode, while data sharing and user revocation are important functions in the cloud storage. Therefore, according to the characteristics of cloud storage, a revocable key-aggregate encryption scheme is put forward based on subset-cover framework. The proposed scheme not only has the key-aggregate characteristics, which greatly simplifies the user’s key management, but also can revoke user access permissions, realizing the flexible and effective access control. When user revocation occurs, it allows cloud server to update the ciphertext so that revoked users can not have access to the new ciphertext, while nonrevoked users do not need to update their private keys. In addition, a verification mechanism is provided in the proposed scheme, which can verify the updated ciphertext and ensure that the user revocation is performed correctly. Compared with the existing schemes, this scheme can not only reduce the cost of key management and storage, but also realize user revocation and achieve user’s access control efficiently. Finally, the proposed scheme can be proved to be selective chosen-plaintext security in the standard model

General Circuit Realizing Compact Revocable Attribute-Based Encryption from Multilinear Maps

This paper demonstrates new technique for managing revocation in the context of attribute-based encryption (ABE) and presents two selectively secure directly revocable ABE (RABE) constructions – supporting decryption policies realizable by polynomial size Boolean circuits of arbitrary fan-out and – featuring compactness in the sense that the number of revocation controlling components in ciphertexts and decryption keys are constant. In fact, our RABE schemes are the first to achieve these parameters. Both our constructions utilize multilinear maps. The size of public parameter in our first construction is linear to the maximum number of users supported by the system while in the second construction we reduce it to logarithmic

Data exploitation and privacy protection in the era of data sharing

As the amount, complexity, and value of data available in both private and public sectors has risen sharply, the competing goals of data privacy and data utility have challenged both organizations and individuals. This dissertation addresses both goals. First, we consider the task of {\it interorganizational data sharing}, in which data owners, data clients, and data subjects have different and sometimes competing privacy concerns. A key challenge in this type of scenario is that each organization uses its own set of proprietary, intraorganizational attributes to describe the shared data; such attributes cannot be shared with other organizations. Moreover, data-access policies are determined by multiple parties and may be specified using attributes that are not directly comparable with the ones used by the owner to specify the data. We propose a system architecture and a suite of protocols that facilitate dynamic and efficient interorganizational data sharing, while allowing each party to use its own set of proprietary attributes to describe the shared data and preserving confidentiality of both data records and attributes. We introduce the novel technique of \textit{attribute-based encryption with oblivious attribute translation (OTABE)}, which plays a crucial role in our solution and may prove useful in other applications. This extension of attribute-based encryption uses semi-trusted proxies to enable dynamic and oblivious translation between proprietary attributes that belong to different organizations. We prove that our OTABE-based framework is secure in the standard model and provide two real-world use cases. Next, we turn our attention to utility that can be derived from the vast and growing amount of data about individuals that is available on social media. As social networks (SNs) continue to grow in popularity, it is essential to understand what can be learned about personal attributes of SN users by mining SN data. The first SN-mining problem we consider is how best to predict the voting behavior of SN users. Prior work only considered users who generate politically oriented content or voluntarily disclose their political preferences online. We avoid this bias by using a novel type of Bayesian-network (BN) model that combines demographic, behavioral, and social features. We test our method in a predictive analysis of the 2016 U.S. Presidential election. Our work is the first to take a semi-supervised approach in this setting. Using the Expectation-Maximization (EM) algorithm, we combine labeled survey data with unlabeled Facebook data, thus obtaining larger datasets and addressing self-selection bias. The second SN-mining challenge we address is the extent to which Dynamic Bayesian Networks (DBNs) can infer dynamic behavioral intentions such as the intention to get a vaccine or to apply for a loan. Knowledge of such intentions has great potential to improve the design of recommendation systems, ad-targeting mechanisms, public-health campaigns, and other social and commercial endeavors. We focus on the question of how to infer an SN user\u27s \textit{offline} decisions and intentions using only the {\it public} portions of her \textit{online} SN accounts. Our contribution is twofold. First, we use BNs and several behavioral-psychology techniques to model decision making as a complex process that both influences and is influenced by static factors (such as personality traits and demographic categories) and dynamic factors (such as triggering events, interests, and emotions). Second, we explore the extent to which temporal models may assist in the inference task by representing SN users as sets of DBNs that are built using our modeling techniques. The use of DBNs, together with data gathered in multiple waves, has the potential to improve both inference accuracy and prediction accuracy in future time slots. It may also shed light on the extent to which different factors influence the decision-making process

Cryptographic Enforcement of Attribute-based Authentication

Doktorgradsavhandling,This dissertation investigates on the cryptographic enforcement about attributebased authentication (ABA) schemes. ABA is an approach to authenticate users via attributes, which are properties of users to be authenticated, environment conditions such as time and locations. By using attributes in place of users’ identity information, ABA can provide anonymous authentication, or more specifically, ABA enables to keep users anonymous from their authenticators. In addition, the property of least information leakage provides better protection for users’ privacy compared with public key based authentication approaches. These properties make it possible to apply ABA schemes in privacy preserving scenarios, for instance, cloud-based applications. The most important security requirements of ABA schemes consist of anonymity, traceability, unforgeability, unlinkability and collision resistance. In this dissertation, we combine these security requirements with other properties such as hierarchy to divide ABA schemes into different categories, based on which we use examples to demonstrate how to construct these schemes cryptographically. The main contributions of this dissertation include the following aspects: We categorize ABA schemes into different types and describe their structures as well as workflows, such that readers can gain a big picture and a clear view of different ABA schemes and their relations. This categorization serves as a guideline how to design and construct ABA schemes. We provide two examples to demonstrate how to construct ciphertext-policy attribute-based authentication (CP-ABA) schemes via two different approaches. Different from key-policy attribute-based authentication (KP-ABA) schemes, attribute keys generated in CP-ABA schemes are comparatively independent of relations among attributes. Thus compared with KP-ABA, CP-ABA extends the flexibility and usage scope of ABA schemes. We extend the core ABA schemes to hierarchical ABA (HABA) schemes by adding the property of hierarchy. Then we propose two different types of hierarchical structures, i.e., user related hierarchical ABA (U-HABA) and attribute related hierarchical ABA (A-HABA). According to these two hierarchical structures, an example is provided for each type to show how to use cryptographic primitives to build HABA schemes. All ABA schemes discussed above and proposed in this dissertation can be implemented to assist users to achieve anonymous authentication from their authenticators. Therefore, these schemes can offer more opportunities to protect users’ privacy, for example, in attribute-based access control (ABAC) and cloud-based services