1. How can cloud storage technology be applied to hospitals for efficient storage and sharing of medical data?
Cloud storage technology can be applied to hospitals to achieve efficient storage and sharing of medical data. With the COVID-19 epidemic in 2020, doctors have been conducting research on COVID-19 through clinical medical data. Accumulating a large amount of medical data, efficiently storing and sharing a large amount of medical data is an urgent problem to be solved. Cloud storage can be combined with medical data to store and share electronic information in the form of medical data in the cloud server. Medical data can also be used as materials to promote the development of medical research in teaching and scientific research. In terms of management, medical data can be used as management information to promote the development of the hospital. However, medical data contain private patient information, and malicious users may obtain patient medical data through illegal means. Therefore, the privacy and security of patient medical data are important issues in data storage and sharing. A common solution is to encrypt medical data, but traditional encryption schemes do not support access control in data sharing, which limits the development of Internet hospitals. The proposed attribute-based encryption (ABE) scheme, specifically ciphertext-policy attribute-based encryption (CP-ABE), solves this problem by allowing data owners to encrypt data using access policies. Hospitals can use CP-ABE to encrypt medical data, with doctors or nurses formulating access policies. Only users with attributes satisfying the access policy can decrypt the ciphertext to obtain plaintext data. However, the traditional CP-ABE scheme stores data in the form of ciphertext and access policy in the form of plaintext, which may lead to privacy leakage. To address this, the proposed scheme combines CP-ABE with symmetric encryption and uses an attribute bloom filter (ABF) to implement access policy hiding and an outsourced decryption scheme to improve computational efficiency. This scheme is suitable for encrypting a large amount of medical data with a fully hidden access policy and high computational efficiency, making it a safe and efficient data-sharing scheme for hospitals.
read more
2. What are the proposed solutions to improve CP-ABE in terms of computational efficiency and data security?
Several researchers have proposed solutions to improve CP-ABE in terms of computational efficiency and data security. Green et al. [14] proposed an ABE scheme with outsourced decryption, which reduces the decryption cost of users by outsourcing part of the pairings to the cloud. Imam et al. [15] conducted a comprehensive study of ABE works associated with e-health data sharing. Jiang et al. [16] proposed attributed-based encryption protection combined with a blockchain to protect electronic health records in edge cloud environments. Zhang et al. [17] proposed a block-based attribute-based keyword searchable encryption scheme for health cloud systems to prevent data forgery and tampering. Li et al. [18] used multikey searchable encryption with attribute-based access control to search encrypted electronic health records in the cloud. Saravanan et al. [19] proposed a novel enhanced attribute-based encryption for secure access in cloud storage for personal health records. Yang et al. [20] addressed the issue of centralized power caused by single-attribute authority in CP-ABE schemes by proposing revocable attribute-based encryption with multiple authorities in the blockchain. Pussewalage et al. [21] introduced a novel attribute-based encryption scheme to share securely outsourced electronic health records with multi-level, controlled-access delegation. Nishide et al. [22] proposed the partially hidden access policy in CP-ABE, while Lai et al. [23] proposed the partially hidden attribute value using complex-order bilinear mapping. Cui [25] added outsourcing decryption to the partially hidden access policy. Hu et al. [26] proposed a 'test-decryption-verification' CP-ABE with an outsourced scheme to reduce decryption calculations. Gan et al. [27] implemented the CP-ABE scheme for vehicular fog computing with partially hidden attributes. Phuong et al. [28] proposed a fully hidden access policy using an element representing a user attribute. Ramu [30] used the ABF method to achieve full hiding of user attributes in CP-ABE for electronic health records. Hao et al. [31] proposed a fully hidden access policy scheme with multi-authority attribute-based encryption. Su et al. [33] introduced a new method of attribute-based encryption supporting hiding policies during electronic medical data sharing. Kim et al. [34] introduced authenticable ABE to resolve attacks on access policy settings. Belguith et al. [35] implemented hidden access policies with outsourced decryption computation. Zhang et al. [36] implemented hidden access policies using outsourced authentication mechanisms to enhance efficiency. These proposed solutions aim to improve computational efficiency and data security in CP-ABE schemes.
read more
3. What is a bilinear map in cyclic groups?
A bilinear map is an effective mapping between two multiplication cyclic groups, G and G1, with prime order p. It is denoted as e: G x G1 -> G1. The map satisfies three properties: non-degeneracy, bilinearity, and computability. Non-degeneracy ensures that e(g, g) is not equal to the unit of G1. Bilinearity states that e(g a, g b) equals e(g, g) multiplied by ab. Computability means that the map can be calculated in polynomial time. This concept is crucial in cryptography and coding theory, where bilinear maps are used for secure communication and error correction.
read more
4. What is a bloom filter used for?
A bloom filter is a data structure used to test whether an element exists in a set. It consists of t hash functions and an m-bit array. Initially, all bits are set to 0. To insert an element e, it calculates the hash function value for all positions and sets the corresponding bits to 1. To check if an element x exists, it calculates the hash function value for all positions. If all corresponding bits are 1, x exists in the array; otherwise, it does not exist. Bloom filters are efficient for membership queries but may produce false positives.
read more