基于健康物联网的轻量级分布式安全数据管理系统
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本文主要探讨了"轻量级健康物联网分布式安全数据管理系统"的研究论文。随着互联网技术的发展，物联网（Internet of Things, IoT）在医疗保健领域的应用日益广泛，它通过网络连接各种传感器和智能设备，实现疾病监测和诊断的高效性。然而，医疗数据的价值高且具有开放性特征，数据隐私保护显得尤为重要。
针对这一挑战，研究者们提出了一个创新的解决方案——轻量级分布式安全数据管理系统。该系统设计的目标是确保在健康物联网环境下，既能保护敏感医疗数据的机密性，又能支持高效的数据搜索和访问控制。具体特点包括：
1. **健康物联网应用**：论文将焦点放在了健康物联网上，强调了其在医疗领域的实际应用，如远程监护、患者数据管理等。
2. **可搜索加密**：为了满足用户对数据检索的需求，系统采用了可搜索加密技术，允许用户通过关键词搜索数据，同时保证数据在解密后不暴露原始内容，实现了隐私保护与信息可用性的平衡。
3. **分布式访问控制**：系统采用分布式架构，通过分散处理和存储数据，降低了单一节点的安全风险，提高了系统的可靠性和安全性。
4. **属性基础加密**：研究人员考虑到了不同用户可能需要访问特定属性的数据，因此引入了属性基础加密方案，根据用户的权限和数据的属性进行精细的访问控制。
5. **轻量级计算**：为了减少系统资源消耗，确保在物联网设备上的高效运行，论文提出了一种轻量级计算方法，优化了加密和解密算法，降低了计算复杂度。
6. **安全性与效率的兼顾**：整体设计旨在在保证数据安全的同时，提升系统的响应速度和用户体验，这对于在医疗环境中广泛应用至关重要。
这篇论文不仅提供了理论框架，还可能包含实际的实现细节和性能评估，为健康物联网的数据管理和安全提供了一个实用且有效的解决方案，对于推动该领域的发展具有重要意义。通过阅读此论文，读者可以了解到如何在物联网环境下构建一个既安全又便捷的数据管理体系。
(2003) to enable single keyword search. Later, Wang et al. (2012)
proposed rank search scheme on single keyword utilizing the relevance
score and order-preserving encryption techniques. Liu et al. (2014)
classified the search queries into multiple ranks to reduce the query
overhead incurred in the cloud. Yu et al. (2013) employed vector space
model and homomorphic encryption methodologies to realize multi-
keyword rank search. Li et al. (2015) concealed the user's access
pattern adopting the blind storage system. The relevance score and k-
nearest neighbor techniques are developed to return the ranked result.
Besides these private searchable encryption schemes, Boneh et al.
(2004) proposed the concept of public key encryption with keyword
search (PEKS) to search on data that is encrypted using the public key
mechanism. Waters et al. (2004) indicated that the PEKS schemes can
be used to construct searchable secure auditing logs. Some research
results (Fang et al., 2013; Guo and Yau, 2015; Chen et al., 2016) tried
to remove secure channel in PEKS systems. The proxy re-encryption
technology is also introduced to PEKS system to realize authority proxy
(Yang and Ma, 2016a, 2016b).
1.2.3. ABE
The first attribute based encryption (ABE) scheme was introduced
by Sahai and Waters (2005) in 2005, in which both the secret key and
ciphertext are associated with a set of attributes. If the user's attribute
matches the attributes embedded in ciphertext, the message can be
recovered from the ciphertext. If the access policy is associated with the
secret key, the ABE scheme will be denoted as KP-ABE (Shi et al., 2015;
Wang and Lang, 2016; Touati and Challal, 2016). If the ciphertext
contains the access policy, the scheme would be called CP-ABE (Odelu
and Das, 2016; Kitagawa et al., 2015; Zhou et al., 2015).
In the seminal work of Sahai and Waters (2005), an open problem
is left to construct a multi-authority ABE system in order to reduce the
burden of the central authority. To deal with this problem, Chase
(2007) proposed a multi-authority ABE system, in which each user has
an identity and interact with the authority using pseudonyms. Han
et al. (2012) introduced a decentralized ABE scheme such that each
authority can issue secret keys independently without knowing any-
thing about the global identifier. Unfortunately, Han's scheme (Han
et al., 2012) was then proved to be insecure by Ge et al. (2013). Later,
Han et al. (2015) proposed another privacy-preserving multi-authority
ABE scheme, in which both the identifiers and the attributes are
protected from the authorities. Zhang et al. (2015) constructed multi-
authority ABE scheme based on the LWE (learning with errors)
problem on lattice to resist quantum attack.
2. Preliminaries
In this section, we present the preliminaries related to bilinear
paring, hardness assumption and notations that are used in our
scheme.
2.1. Bilinear groups
Let
p
.
be an algorithm that on input the security parameter λ .It
outputs the parameters of a prime order bilinear map as
pgGG e(, , , ,
)
T
, where G and G
T
are multiplicative cyclic groups of
prime order p and g is a random generator of G. The mapping
eG G G:×→
T
is a bilinear map. The bilinear map e has three
properties: (1) bilinearity:
uv
G
∀, ∈
and
ab Z,∈
p
, we have
eu v euv(, )=()
ab ab
. (2) non-degeneracy:
eg g(, )≠
1
. (3) computability:
e can be efficiently comnputed.
2.2. Hardness assumptions
Assumption 1 (DBDH: decisional bilinear Diffie-Hellman
assumption). . Let G be a bilinear group of prime order p and g be a
generator of G. Let
α
βγ Z,, ∈
*
p
be chosen at random. If an adversary
(
is given
ygggg
→
=( , , ,
)
αβγ
, it is hard for the attacker
(
to distinguish
eg g G(, ) ∈
αβγ
T
from an element Z that is randomly chosen from G
T
.
2.3. Notations
The main notations used in this paper are listed in Table 1.
3. System and security model
3.1. System architecture
The system architecture of LDAC-KS for distributed health IoT
system is shown in Fig. 1, which involves the following entities.
•
health Io
T
(
data owner
). The heterogenous health IoT network con-
tains various subsystems, such as the body sensor network (BSN),
home smart rehabilitation system and hospitals. The smart devices
in these subsystems are connected and communicated with each
other to collect and transmit data. For instance, the tiny sensors in
BSN are used to detect heart rate, the pulse and oxygen in blood,
body temperature, electrocardiogram (ECG) and pumped insulin
etc. The vital physiological information is detected and aggregated to
be patient's EHR. These sensitive medical information will be sent to
the cloud server through internet. For the sake of privacy protectoin,
the EHR has to be encrypted before outsourcing. In order to support
efficient keyword search on huge health documents, the keywords
has to be extracted and encrypted into keyword index to facilitate
the data retrieval.
•
Attribute Authoritie
s
. There exists multiple attribute authorities (AA)
in the distributed IoT system. They are responsible to authenticate
the users and then create the attribute public and private keys for
the users in their management domain.
•
Cloud Serve
r
. The cloud server in the architecture is responsible for
the health data storage and respond on the keyword search query. It
also possesses almost infinite storage capability and computation
resources (Chang et al., 2016b; Chang, 2014). In order to accelerate
the search speed on the big data, it is important to reduce the
computation overhead in test algorithm to find the match docu-
ments rapidly. Meanwhile, the cloud server is regarded as honest-
but-curious, who is honest to operate the designated calculations
and curious to the plaintext in the encrypted EHR and the searched
keyword trapdoor.
•
Data user. There are multiple kinds of users in this architecture,
including the heath care providers, the friends and family of the
patients as well as third party organizations (for instance, insurance
company). Each user in the system will be endowed a set of
attributes to describe his characteristics. In order to issue keyword
Table 1
Notations.
Notation Descryption
PK M
K
/
public key/secret key of the system
PK SK/
u
i
u
i
public key/secret key of user u
i
SK
u
i
out
outsourced secret key of user u
i
PK SK/
A
i
A
i
public key/secret key of authority A
i
PK SK/
attr
i
attr
i
public key/secret key of attribute attr
i
Q
K
u
i
keyword query key for user u
i
CT ciphertext of message
CT
out
outsourced ciphertext
CT
transf
transformed ciphertext
I
KW
encrypted keyword index
K
WT/
K
W
keyword/keyword trapdoor
T
KW
out
outsourced keyword trapdoor
/attr access policy/attribute
S
u
i
attribute set of user u
i
Y. Yang et al.
Journal of Network and Computer Applications 89 (2017) 26–37
28
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