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An efficient attributebased hierarchical data access control scheme in cloud computing
Humancentric Computing and Information Sciences volume 10, Article number: 49 (2020)
Abstract
Security issues in cloud computing have become a hot topic in academia and industry, and CPABE is an effective solution for managing and protecting data. When data is shared in cloud computing, they usually have multiple access structures that have hierarchical relationships. However, existing CPABE algorithms do not consider such relationships and just require data owners to generate multiple ciphertexts to meet the hierarchical access requirement, which would incur substantial computation overheads. To achieve finegrained access control of multiple hierarchical files effectively, first we propose an efficient hierarchical CPABE algorithm whose access structure is linear secret sharing scheme. Moreover, we construct an attributebased hierarchical access control scheme, namely AHAC. In our scheme, when a data visitor’s attributes match a part of the access control structure, he can decrypt the data that associate with this part. The experiments show that AHAC has good security and high performance. Furthermore, when the quantity of encrypted data files increases, the superiority of AHAC will be more significant.
Introduction
The advent of the mobile Internet era has brought data sharing into people’s daily life, and the relevant platforms are also widely used, like Facebook, Badoo and MySpace. In the meantime, cloud computing has become a promising technology used for massive data sharing [1]. Before sharing data, users usually choose to encrypt data to protect their security. One traditional method is to use symmetric encryption, the other is to use public key encryption [2,3,4]. Nevertheless, there are some problems with these methods. Some of them cannot achieve flexible access control [2], some schemes are poor in performance [3], and some have defects in security [4]. Therefore, attributebased encryption (ABE) [5] was proposed to overcome these problems in unreliable storage environment. The access control strategy of ciphertextpolicy ABE (CPABE) is encrypted into the ciphertext [6]. This feature makes it very suitable for data sharing. In CPABE, the time of plaintext encryption is only linearly proportional to the attribute number, so it’s efficient. Shamir secret sharing scheme [6] is the foundation of traditional CPABE algorithm. In 2011, a more efficient algorithm based on linear secret sharing scheme (LSSS) was proposed [7].
In actual application scenarios, shared data files usually have multiple access structures that have hierarchical relationships. This relationship is common in the health and military fields. Most CPABE do not consider this hierarchical relationship and just require data owners to generate multiple ciphertexts to encrypt these files, which would incur substantial computation overheads. Wang proposed a File Hierarchy ABE scheme (FHCPABE) [8], which integrates multiple different access structures with hierarchical relationships into a single access structure. When a data visitor’s attributes match the partial access structure, he can decrypt the data that associate with this part. Only when the entire access structure is satisfied, all the data can be decrypted. Since the data owner does not need to generate multiple access structures and ciphertexts, the efficiency is greatly improved. However, FHCPABE uses a tree access structure, so its efficiency is still low.
In this article, our contributions are as follows:

(1)
We design a hierarchical CPABE algorithm whose access structure is LSSS matrix. In the algorithm, multiple hierarchical access control structures of data files are integrated into a single LSSS matrix, so all the data are encrypted into an entire ciphertext.

(2)
Based on the proposed CPABE algorithm, we construct an Attributebased Hierarchical data Access Control scheme (AHAC) in the cloud computing. In AHAC, we achieve efficient and flexible access control. When a data visitor’s attributes match a part of the access control structure, he can decrypt the data that associate with this part. Moreover, the scheme just requires one operation of encryption and decryption to complete the work that traditional schemes have to do multiple encryption and decryption.

(3)
We conduct security analysis and performance evaluation for AHAC. Security analysis shows that AHAC has prominent security features. Performance evaluation demonstrates that the private key production time and storage cost of our scheme are only 25 percent of FHCPABE, and the encryption and decryption time and ciphertext storage cost also have advantages.
The remaining parts of this paper are organized as follows. In “Related work” section, we introduce some related work in this field. In “Preliminaries” section, we introduce preliminaries which contain some notions and definitions. Then, the detailed construction of AHAC is presented in “AHAC: attributebased hierarchy data access control scheme” section. In “Security analysis and performance evaluation” section, we provide security analysis and performance evaluation. Finally, the conclusions are given in “Conclusions” section.
Related work
The fuzzy identitybased encryption [5] put forward by Sahai and Waters in 2005 is the prototype of ABE. The basic ABE can only represent the “threshold” operation of attributes, and the threshold parameters are set by the authorized authority rather than by the sender. Cheung realized the first CPABE scheme, which can just support “AND” gate access control strategy [9]. To implement a more flexible strategy, a new CPABE was designed by Bethencourt. His scheme applies the tree access control structure to realize the “AND”, “OR”, and “OF” strategy, and achieves finegrained access control [6]. However, it cannot provide strong security. In 2008, Goyal and Jain put forward a CPABE that has selectively security in the decisionalBilinear DiffieHellman (dBDH) assumption [10]. Nevertheless, the time consumption by encryption and decryption, and the sizes of its private key and ciphertext grow up by n^{3.42} (n represents the attribute number associated with the access control tree), which limits its practicability. In 2011, Lewko and Waters proposed a technology which can transform an access control tree to an LSSS representation. This technique makes it possible to replace tree structure with matrix structure. Thus, in the same year, Waters designed a CPABE scheme using matrix structure [7]. Its time consumption by encryption and decryption, and the sizes of private key and ciphertext increase linearly with its attribute number. Besides, the scheme has selectively security in the decisional qparallel BDH Exponent (dParallel BDHE) assumption [7]. Some schemes [11,12,13,14,15] have applied CPABE to realize file access control in the cloud. There are also some schemes to improve the algorithm itself, such as [16, 17] fix the ciphertext size to improve performance, and [18, 19] improve security through authority control or accountability, and [20,21,22] support attribute revocation to improve practicability. Scheme [23] supports proxy computing to private servers, and [24] supports hidden access policy, and [25] proposes a lightweight and efficient CPABE. However, none of them consider the hierarchical access relationships of multiple shared files.
Researchers also proposed some hierarchical CPABE based on tree or LSSS matrix structure. The schemes proposed in [26,27,28] use multiple hierarchical authorized organizations to create secret keys cooperatively for users, and alleviate the burden of a single authority center. In [29,30,31], schemes without central authority were further proposed, which improved the system security. In [32], there is a hierarchical relationship between attributes, and attributes with high permission can replace the attributes with low permission when decrypting. In [8], FHCPABE is proposed for cloud data access control, and an integrated tree access structure is used for encrypting all the data. However, its efficiency is still not high. It should be noticed that in our scheme, we focus on the issue of hierarchical access relationships of multiple shared files, which is the same as [8].
Preliminaries
First of all, we present the related preliminaries of AHAC, then we describe an example of using these techniques to implement hierarchical access control, and last we give the definition of dParallel BDHE.
Hierarchical sccess control
In the traditional CPABE scheme, users’ attributes either satisfy the access control structure to obtain plaintext, or do not satisfy the access control structure to obtain plaintext. As shown in Fig. 1, only user 1 and 4 can recover the plaintext, because their attributes match the access control structure.
In hierarchical access control, multiple different access structures with hierarchical relationships can be integrated into a single access structure. As shown in Fig. 2, T_{1}, T_{2} represents the access structures of m_{1}, m_{2} accordingly, and obviously they have hierarchical relationship, so they can be integrated into a single access structure T. As shown in Fig. 3 when a data visitor’s attributes match the partial access structure, he can decrypt the data that associate with this part (User 2). Only when the entire access structure is satisfied, all the data can be decrypted (User 1). Since the data owner does not need to generate multiple access structures and ciphertexts, the efficiency is greatly improved.
Linear secret sharing scheme
Beimel first proposed the definition of LSSS in paper [33]: A secret sharing scheme Π over a collection of parties P is described linear on Z_{p} when:

(1)
The shares of all the parties make up a vector on Z_{p}.

(2)
Such a matrix M for Π is existed, which is used for producing shares. M has l rows and n columns. For \( i = 1,2, \ldots ,l \), the ith row M_{i} of M is marked by a party \( \rho (i) \) where function \( \rho \) satisfies: \( \{ 1,2, \ldots ,l\} \to e \). Given a column vector \( \vec{v} = (s,r_{2} , \ldots ,r_{n} ) \), in which \( s \in Z_{p} \) is the shared secret and \( r_{2} , \ldots r_{d} \in Z_{p} \) are randomly chosen, \( M\vec{v} \) is the vector constructed by m shares of s decided by Π. The share \( \lambda_{i} = (M\vec{v})_{i} \) is part of party \( \rho (i) \).
It is shown in [33] that each LSSS has the linear reconstruction feature: Assume that there exists an LSSS Π corresponding to the access structure T, and S∈T is an arbitrary authorized set, \( I \subset \{ 1, \ldots ,l\} \) is denoted as \( I = \{ i:\rho (i) \in S\} \). There are constants \( \{ \omega_{i} \in Z_{p} \}_{i \in I} \) that makes \( \sum\nolimits_{i \in I} {\omega_{i} \lambda_{i} } = s \), in which \( \{ \lambda_{i} \} \) are shares of arbitrary secret s decided by Π. In addition, \( \{ \omega_{i} \} \) will be found under polynomial time in the size of the sharegenerating matrix M.
There will exist a vector like that \( \omega \cdot (1,0, \ldots ,0) =  1 \) and \( \omega \cdot M_{i} = 0 \) for all \( i \in I \) for any unauthorized set of rows I.
It can be obtained by mathematical derivation for a randomly selected vector \( \vec{v} = (s_{1} , \ldots ,s_{j} , \ldots ,s_{n} ) \), where \( s_{j} \in Z_{p} \) is the jth secret of the n secrets that need to be recovered, and it corresponds to a nonleaf node in the tree structure. When recovering a secret, if the set of attributes possessed can satisfy this nonleaf node, then \( \{ \omega_{i} \in Z_{p} \}_{i \in I} \) will be found under the polynomial time which satisfies \( \sum\nolimits_{i \in I} {\omega_{i,j} M_{i}^{T} } = \varepsilon_{j} \), where \( \varepsilon_{j} \) is a row vector whose length is n with the jth element is 1 and the remaining elements are 0. Then we can get \( s_{j} = \sum\nolimits_{i \in I} {\omega_{i,j} \lambda_{i} } \).
Marking method to construct LSSS matrix
Beimel proved that the access control strategy described by tree structure can be converted to matrix M in LSSS, but no specific conversion method is given in [33]. Until 2011, Lewko and Waters presented a construction method for an LSSS matrix in [34]: Given an access tree defined by a Boolean formula, it can be converted to an LSSS matrix by a marking method. And any one of the propositional paradigms can find its Boolean formula. The specific conversion method can be found in [33].
An example of hierarchical access control using LSSS matrix
There is a hierarchical access tree T which is shown in Fig. 2, and its Boolean formula is (A AND (B AND (C OR D))). We can use the above marking method to convert it to an LSSS matrix by Formula 1 as:
Next, we give an example of how to use the LSSS matrix to achieve hierarchical access control.
When encrypting, we randomly select a vector \( \vec{v} = (s_{1} ,s_{2} ,s_{3} ) = (2,5,3) \), in which \( s_{1} ,s_{2} ,s_{3} \) are secrets assigned to the nonleaf nodes in Fig. 2. Then we can calculate λ by Formula 2:
From “Linear secret sharing scheme”, we know \( s_{j} = \sum\nolimits_{i \in I} {\omega_{i,j} \lambda_{i} } \), where \( I = \{ i:\rho (i) \in S\} \),\( \rho (i) \) can convert the ith row into the attribute represented by this row, and S is the user’s attribute set. Thus, we can get Formula 3:
Obviously, we must get \( \omega_{j} \) if we want to get \( s_{j} \), then we make the following formula 4 derivation:
We make \( M_{A}^{T} \omega_{j} = \varepsilon_{j} \), so \( s_{j} = \vec{v} \cdot \varepsilon_{j} \). Then we can compute \( \varepsilon_{j} \) as a row vector whose length is n with the jth element is 1 and the remaining elements are 0.
When decrypting, if a decryptor only has the attributes B, C, i.e., it only satisfies the partial access structure, then he can get \( \omega_{2} ,\omega_{3} \) by Formula 5 and 6:
Thus, \( \omega_{3} = \left( {\begin{array}{*{20}c} 0 \\ {  1} \\ \end{array} } \right) \), \( \omega_{2} = \left( {\begin{array}{*{20}c} {  1} \\ {  1} \\ \end{array} } \right) \). Finally, he can get s_{3} and s_{2} from Formulas 7 and 8:
Similarly, if the decryptor has the attributes A, B, and C, then he satisfies the entire access structure, and all the secrets \( s_{1} ,s_{2} ,s_{3} \) can be computed by the above steps.
AHAC: attributebased hierarchy data access control scheme
In the chapter, first we give the overview and the security assumptions of AHAC. After that, we design the core algorithm of AHAC, namely AHACCPABE. Finally, we present the system operations of AHAC detailedly.
Scheme overview
The system framework of AHAC is shown in Fig. 4. Firstly, central authority (CA) performs the system initialization operation and generates system attributes and relevant keys. Then, double encryption mechanism are used to promote the efficiency, that is, data owner chooses n symmetric keys \( \{ ck_{1} , \ldots ,ck_{n} \} \) to encrypt the data files \( \{ f_{1} , \ldots ,f_{n} \} \) respectively using a symmetric encryption algorithm (AES, DES, etc.), and encrypts \( \{ ck_{1} , \ldots ,ck_{n} \} \) using AHACCPABE algorithm. The symmetric encryption algorithm with high efficiency is used to encrypt the files of large volume, and the CPABE algorithm is used to encrypt the symmetric key of small volume. Compared with the symmetric encryption algorithm, the performance of CPABE algorithm is relatively lower. However, the CPABE algorithm can bring the obvious advantage in key management, using which we can easily implement the access control of encrypted data. Thus, we utilize such double encryption method to achieve the secure, efficient and finegrained data access control in the cloud.
The user then transfers the two ciphertexts to cloud server (CS) and CS stores them for sharing. When a data visitor wishes to obtain the data files, he should contact CA and CA distributes corresponding private keys to him according to his attributes. Then, this data visitor obtains the ciphertexts from CS. When his attributes match partial or entire access control structure, he can decrypt the symmetric keys that associated with this part. At last, the data visitor is able to get the corresponding files using the symmetric keys. It is clear from our framework that only one encryption and decryption operation is needed to share multiple files securely, while traditional schemes have to do multiple encryption and decryption operations.
Security assumptions
In this section, we will present security assumptions for several entities in the system.
We consider that CS is honest but curious in AHAC like the related work [35] do, that is, CS will honestly perform the task of private key distribution yet it is also trying to gain the contents of the data files and symmetric keys stored in it. Besides, CS is online all the time to provide stable services.
CA is fully trusted and is online all the time. There is a security approach for CA to transfer private key to users. Users can get the services of the system at any time.
For any number of unauthorized users, they may launch collusion attacks and try to obtain the confidential data.
AHACCPABE
The AHACCPABE includes four functions: system initialization, private key production, encryption and decryption. These functions make the following cases: when a data visitor’s attributes match a part of the access control structure, he can decrypt the data that associate with this part, and when the entire access structure is satisfied, all the data can be decrypted. Here are the details of the algorithm:

(1)
System initialization
Function 1 takes an attribute set U of system and a parameter k specifying the system security as input, and produces a system master key MK and a corresponding public key PK.

(2)
Private key production
As shown in Function 2, it inputs PK, MK, and the attribute set S of a user, and produces a user private key SK that is related to S.

(3)
Encryption
As shown in Function 3, the encryption function inputs a plaintext set \( \{ m_{j} ,j \in (1,n)\} \), PK, and an LSSS matrix structure \( (M,\rho ) \), and returns a ciphertext CT. For an LSSS matrix structure \( (M,\rho ) \), the dimension of M is \( l \times n \), M_{i} is the ith row of M, and \( \rho (i) \) can convert M_{i} into the attribute represented by it.

(4)
Decryption
As shown in Function 4, CT and SK are inputs, and outputs is plaintext set \( m_{j} \). M_{A} is a matrix composed of a set of row vectors in M that corresponds to the attribute set S associated with SK. \( \varepsilon_{j} \) is a row vector with length n, in which the jth element is 1 and the remaining elements are 0. \( I = \{ i:\rho (i) \in S\} \).
The detailed operation process of AHAC
AHAC consists of six operations: System initialization, encryption of data files, encryption of symmetric keys, user authorization, decryption of symmetric keys and decryption of data files.

(1)
System setup
CA designates an attribute set U and invokes Function 1 to produce a master key MK and a public key PK, and MK is safely stored in CA.

(2)
Encryption of data files
Data owner (DO) chooses n symmetric keys \( \{ ck_{1} , \ldots ,ck_{n} \} \) to encrypt his data files \( \{ f_{1} , \ldots ,f_{n} \} \) by a symmetric encryption algorithm respectively. The data file ciphertext are denoted as: \( EF = \{ E_{{ck_{1} }} (f_{1} ), \ldots ,E_{{ck_{n} }} (f_{n} )\} \).

(3)
Encryption of symmetric keys
DO defines access trees \( \{ T_{1} , \ldots ,T_{n} \} \) for his data files \( \{ f_{1} , \ldots ,f_{n} \} \) respectively and integrates them into a single access tree T. Then, he uses marking method to converted T to LSSS matrix structure \( (M,\rho ) \). Next, he calls Function 3 to encrypt his symmetric keys \( \{ ck_{1} , \cdots ,ck_{n} \} \) and generates a symmetric key ciphertext CT. Finally, he sends CT and EF to CS and CS stores them.

(4)
User authorization
For any data visitor, CA specifies a set S of attributes and calls Function 2 to output the corresponding private key SK.

(5)
Decryption of symmetric keys
When a user wants to obtain some files from CS, CS first checks whether his attributes match partial or entire access control structure of those data files. If not, CS refuses the user’s request; otherwise, CS sends CT to the user. After obtaining CT, the user calls Function 4 to get the symmetric keys. When his attributes satisfy a part of the access tree, he can decrypt the symmetric keys that associated with this part, assuming \( \{ ck_{1} , \ldots ,ck_{n} \} \). Only when his attributes match the entire access control structure, he can obtain all the symmetric keys.

(6)
Decryption of data files
In the last step, the user downloads \( \{ E_{{ck_{1} }} (f_{1} ), \ldots ,E_{{ck_{n} }} (f_{n} )\} \) and uses \( \{ ck_{1} , \ldots ,ck_{n} \} \) to decrypt the data files \( \{ f_{1} , \ldots ,f_{n} \} \) by the symmetric decryption algorithm.
To further improve the efficiency, we make the following transformation:
where \( \{ ck_{1} , \ldots ,ck_{n} \} \) are n symmetric keys. After then, we call Function 3 to encrypt \( \{ ck_{1}^{'} , \ldots ,ck_{n}^{'} \} \) and generates a symmetric key ciphertext CT. When decrypting, we call Function 4 to get the symmetric keys. In Function 4, once we successfully decrypt a \( ck_{j}^{'} \), we can stop the decryption process immediately, since \( ck_{j}^{'} \) contains all the contents of the rest symmetric keys.
Security analysis and performance evaluation
In this chapter, we give the analysis for the security and the evaluation results for the performance.
Security analysis
We give the security features of AHAC based on the security assumptions presented in chapter 4.2, containing data confidentiality, collusion defense and finegrained access control.

(1)
Data confidentiality
AHACCPABE algorithm is designed on top of Waters’s algorithm [7]. The security of his scheme is based on dParallel BDHE assumption.
dParallel BDHE assumption: Select a bilinear group G of prime order p with generator g, and select \( \beta ,s,b_{1} , \ldots ,b_{q} \in Z_{p} \) at random. Even if the adversary gets
it’s hard for him to get \( e(g,g)^{{\beta^{q + 1} s}} \in G_{T} \).
There exists a main difference between AHACCPABE algorithm and his algorithm. In AHACCPABE, we use all the elements in the secret vector \( \vec{v} \) to allow multiple secrets to be carried in an access control policy, under which multiple plaintexts are encrypted. That is to say, AHACCPABE exploits all the elements in vector \( \vec{v} \), using each of them to encrypt every plaintext respectively, as shown in Function 3, whereas in Waters’s CPABE algorithm, just one element in the vector is used for encrypting a plaintext [7] and for multiple plaintexts, their algorithm needs to be executed multiple times. In [7], Waters’s CPABE algorithm has the selectively security in dParallel BDHE assumption. Therefore, AHACCPABE has the same security under the same assumption.
In AHAC, data files are encrypted using symmetric encryption keys, and these keys are then encrypted using AHACCPABE. In this mechanism, just the ciphertexts of the files and the ciphertexts of the keys are given to cloud servers. Since the used symmetric encryption algorithm, such as AES, is secure, the security of this mechanism merely relies on the security of AHACCPABE. In the above paragraph, we have shown that AHACCPABE is secure under dParallel BDHE assumption. Thus, the AHAC is secure under the same model.

(2)
Collusion defense
Any number of unauthorized users may launch collusion attacks, trying to access the confidential data files. In AHACCPABE, CA chooses an element t randomly for each user and uses t to generate a private key for each of them. When a user decrypts a ciphertext, he should compute \( e(g,g)^{{\alpha s_{j} }} \) first, which requires the components of his private key contain the same t. That is to say, different data visitors can’t integrate their private keys to strengthen their decryption power, since they have different values of t in private keys. Therefore, AHAC can resist collusion attacks effectively.

(3)
Finegrained access control
In AHAC, the LSSS matrix access structure is transformed from an access tree which supports “AND” “OR”, and “OF” threshold operations, and it can represent any complex access control policy. Only data visitors who own the attributes matching the access control structure can obtain the plaintext successfully. Thus, AHAC realizes finegrained access control.
Performance evaluation
We evaluate the performance of AHACCPABE from two aspects: its time costs, and the storage costs of ciphertext and private key. Both are compared with those of traditional CPABE [6], LSSSbased CPABE (hereinafter referred to as LSCPABE) [7], and FHCPABE [8].
We make the following access policy: assume that the plaintext \( M = (m_{1} ,m_{2} , \ldots ,m_{n} ) \), for the traditional CPABE and LSCPABE, n policies are needed respectively for \( m_{1} ,m_{2} , \ldots ,m_{n} \) as:
Policy(1): \( \{ (att_{1} ,att_{2} , \ldots ,att_{i} ,j\;of\;i)\;AND\;att_{i + 1} \;AND\;att_{i + 2} \;AND \cdots AND\;att_{i + n  1} \} \)
Policy(2): \( \{ (att_{1} ,att_{2} , \ldots ,att_{i} ,j\;of\;i)\;AND\;att_{i + 1} \;AND\;att_{i + 2} \;AND \cdots AND\;att_{i + n  2} \} \)
Policy(n−1): \( \{ (att_{1} ,att_{2} , \ldots ,att_{i} ,j\;of\;i)\;AND\;att_{i + 1} \} \)
Policy(n): \( \{ att_{1} ,att_{2} , \ldots ,att_{i} ,j\;of\;i\} \; \)
FHCPABE and AHACCPABE only need one access policy with n access structure level as:
Policy: \( \{ (att_{1} ,att_{2} , \ldots ,att_{i} ,j\;of\;i)\;AND\;att_{i + 1} \;AND\;att_{i + 2} \;AND \ldots AND\;att_{i + n  1} \} \)
In Table 1, we compare the performance of four CPABE algorithms by theoretical calculation. \( \mu \) represents the global attribute set, \( \omega \in \mu \) represents the attribute information contained in the user’s private key, c represents the attribute contained in the access structure, n represents the access structure hierarchy, the power operation on the group G_{0} is E_{0}, the power operation on the group G_{T} is E_{T}, and the multiplication calculation on the group is M. P represents the pairing operation in group G_{0}. The element size on group G_{0} is represented as l_{0}, and the element size on group G_{T} is represented as l_{T}. Due to the trivial time consumption of hash operation, the time consumption of hash is ignored. As shown in Table 1, AHACCPABE has high performance in all aspects.
We conduct detailed experiments to simulate the complete access control process, in which all four algorithms are implemented based on JPBC [36]. In the experiments, a super singular elliptic curve \( y^{2} = x^{3} + x \) is adopted of which the group order is 160 bits on a 512bit finite field. The experiments are performed on a computer with Pentium G4560 3.50 Hz processor, and 8.00 GB RAM. We take the average of 10 experiments as results to make them more accurate.
The private key generation time of four algorithms have been shown in Fig. 5. As the attribute number increases, the private key production time costs and the private key storage costs of AHACCPABE and LSCPABE grow slower than those of the other two algorithms. This will significantly reduce the pressure of CA.
Figure 6 shows the encryption and decryption time costs with two fixed access structure levels as attributes increase. We can see that the time costs by encryption and decryption of AHACCPABE and FHCPABE are always less than those of the other two algorithms.
Figure 7 shows the encryption and decryption time costs with different access structure level and fixed attribute number N = 30 respectively. It’s obvious that the encryption and decryption time costs of FHCPABE and AHACCPABE are constants when the number of access structure levels increases, while in traditional CPABE and LSCPABE there are rapid linear growth in the time costs.
From Figs. 5, 6 and 7, we can conclude that the time consumptions by encryption and decryption of AHACCPABE are still less than those of FHCPABE. However, in the cloud environment with big data, the gap of them will be widened. Moreover, the private key production time consumption by private key production of AHACCPABE is much less than that of FHCPABE.
Figure 8 shows the storage cost of private key. As the attribute number increases, the private key storage costs of AHACCPABE and LSCPABE grow slower than those of the other two algorithms.
Figure 9a shows the storage cost of ciphertext with two fixed access structure levels as attributes increase. We can see that the ciphertext storage costs of FHCPABE and AHACCPABE are very close, while the costs of traditional CPABE and LSCPABE are about twice as those of them, since in this experiment, the access structure level is set to two. Figure 9b shows the storage cost of ciphertext with different access structure level and fixed attribute number N = 30 respectively. We can see that the ciphertext storage costs of AHACCPABE and FHCPABE increase slightly when the number of access structure level increases, and the ciphertext storage costs of traditional CPABE and LSCPABE increase sharply.
From Figs. 8 and 9, we can conclude that the ciphertext storage consumption of AHACCPABE is still less than that of FHCPABE, and furthermore the private key storage consumption of AHACCPABE is obviously less than that of FHCPABE.
Conclusions
Most of existing data access control schemes of CPABE do not consider the hierarchical access relationships of multiple shared data files, and just need data owners to generate multiple ciphertexts to meet the hierarchical access requirement, which would incur substantial computation overheads. To solve this problem, we first give an efficient hierarchical CPABE algorithm based on LSSS and furthermore, we construct AHAC, which uses an integrated access structure that makes users be able to encrypt multiple data files with hierarchical access relationships at once. When a data visitor’s attributes match a part of the access control structure, he can obtain the data that associate with this part by just one decryption. In addition, AHAC is secure, and has very low costs both in computation and storage aspects compared with related works.
In the future, we will work towards using blockchain technology to expand the single authority to multiple authorities, improve the security and stability of the authority, and support the accountability of authority.
Availability of data and materials
All data generated or analyzed during this study are included in this published article [and its supplementary information files]
Abbreviations
 CPABE:

Ciphertextpolicy attributebased encryption
 AHAC:

Attributebased hierarchical data access control scheme
 ABE:

Attributebased encryption
 LSSS:

Linear secret sharing scheme
 PHR:

Personal health record
 FHCPABE:

File hierarchy attributebased encryption scheme
 dBDH:

DecisionalBilinear DiffieHellman
 dParallel BDHE:

Decisional qparallel Bilinear DiffieHellman Exponent
 CA:

Central Authority
 CS:

Cloud server
 DO:

Data owner
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This work was supported by the National Natural Science Foundation of China under Grant Nos. 61602351, 61802286, 61502359, the Hubei Provincial Natural Science Foundation of China under Grant No. 2018CFB424.
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Conceptualization HH and LZ; Implementation HH, LZ, and PL; Validation LD, LH, and XC; Writing and editing HH and LZ. All authors read and approved the final manuscript.
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He, H., Zheng, Lh., Li, P. et al. An efficient attributebased hierarchical data access control scheme in cloud computing. Hum. Cent. Comput. Inf. Sci. 10, 49 (2020). https://doi.org/10.1186/s13673020002555
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DOI: https://doi.org/10.1186/s13673020002555
Keywords
 Cloud computing
 Attributebased encryption
 Hierarchical access structure
 Linear secret sharing