医疗云中的可搜索加密技术：一项调查

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"本文是关于医疗云中可搜索加密技术的研究论文综述，探讨了如何在保护数据隐私和访问隐私的同时，实现对外包至第三方云端的医疗数据进行搜索服务。文章作者为Rui Zhang, Rui Xue和Ling Liu（IEEE会员）。" 在医疗领域，将电子健康记录（EHR）系统外包给第三方云服务已经成为一种趋势，因为它能降低前端拥有成本和IT维护负担。然而，这种外包模式也带来了数据隐私和访问隐私保护的问题。为了在云计算环境中确保医疗数据的安全，可搜索加密技术应运而生。该技术旨在使医疗服务提供者能够与授权用户共享加密数据，并允许对加密数据进行查询，同时保持数据的机密性。可搜索加密技术的核心目标是让数据在加密状态下仍能进行有效搜索，而不泄露任何敏感信息。这涉及到两个关键能力：一是数据隐私保护，即只有授权用户可以解密并查看数据；二是访问隐私保护，这意味着云服务提供商无法获取查询的具体内容，只知道哪些数据匹配了查询条件。文章可能深入讨论了以下几方面的内容： 1. **加密算法与数据结构**：为了实现可搜索加密，通常需要设计特定的加密算法和数据结构，如索引结构，以便在加密数据上执行高效的搜索操作。 2. **查询类型支持**：可搜索加密技术可能支持多种查询类型，包括精确匹配、范围查询、模糊查询等，以满足不同医疗应用的需求。 3. **效率与安全性权衡**：在保证安全性的前提下，如何提高查询效率是一大挑战。文章可能会分析各种技术的性能瓶颈和改进策略。 4. **隐私保护机制**：包括如何防止中间人攻击、重放攻击等，以及如何确保只有授权用户才能访问和解密数据。 5. **现有方案的优缺点**：文中可能对比分析了现有的可搜索加密方案，评估它们在实际医疗云环境中的适用性和局限性。 6. **未来研究方向**：最后，作者可能会讨论该领域的未来研究方向，如增强隐私保护、提高系统性能、适应更复杂的查询需求等。这篇论文全面概述了医疗云中可搜索加密技术的现状、挑战和未来趋势，对于理解医疗数据安全保护的重要性，以及推动相关技术的发展具有重要意义。

1939-1374 (c) 2017 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.

This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TSC.2017.2762296, IEEE

Transactions on Services Computing

denote hospital 1 and h2 to denote hospital 2. The children

nodes of role nodes are medical diagnosis nodes and other

laboratory inspection and test nodes. Only these leaf nodes

contain EHR data, such as prescriptions and diagnosis and

so on. Thus, the tree has two types of nodes: leaf nodes

that contain real EHR data and the internal nodes of upper

levels that are actually indices for EHR data. The former

nodes are called data nodes, which are represented by the

rectangle, and the latter nodes are called index nodes, which

are represented by the oval. Obviously in this structure, all

data nodes are nested according to a role node, so that they

can be expediently retrieved by different roles of doctors.

In the preceding EHR data structure, the index nodes

in level 2 can be used as the keywords for search. When

the cloud ﬁnds the matched index nodes according to the

queried keywords, it returns the data nodes to the data

users. However, in such an inverted index-based structure,

not only the EHR data need to be encrypted when they

are stored in a remote healthcare cloud, but also the index

should be encrypted, since it is easy to infer sensitive

information about the patient from the index nodes. In this

paper, searchable encryption schemes are used to encrypt

the index and support search operations over encrypted da-

ta. The encryption scheme used to encrypt EHR data can be

any secure searchable encryption scheme. The data owner

can choose different encryption schemes based on different

healthcare application scenarios and their respective privacy

requirements.

In the rest of this paper, we let D = {d

, d

, · · · , d

}

denote the set of EHR data to be stored in a third party and

possibly untrusted healthcare cloud, where |D| = m is the

total number of EHR data. According to the preceding EHR

data structure, each EHR data d

is indexed by one or more

keywords. Let I = {w

, w

, · · · , w

} be the index in D,

where |I| = n is the total number of keywords. D

i∈[n]

⊆ D

denotes the set of EHR data are indexed by keyword w

The keywords and the sets of EHR data are respectively

encrypted by encryption algorithms Enc and E, and are

stored in the healthcare cloud. Note again that Enc and E are

different encryption schemes: Enc is a searchable encryption

scheme and E can be any secure encryption scheme chosen

by the data owner.

2.2 Privacy Requirements in SE Schemes

Intuitively, an SE scheme is secure if the server learns

nothing about the query as well as the documents except

the encrypted query results [1], [4], [5], [6]. In this section,

we discuss in detail the speciﬁc privacy requirements for

index-based data storage structures, where the cloud server

searches over a set of searchable index instead of searching

on encrypted data directly.

Data privacy is a basic requirement for the EHR data such

that the outsourced EHR data should not be revealed in

any form to any unauthorized parties, including the cloud

service providers. Typically, it can be guaranteed by the

encryption algorithms. The user who has the secret key can

effectively decrypt the encoded EHR data after retrieving

them from the cloud server. The encryption based protection

needs to consider the following three types of requirements

for privacy protection: (i) leakage due to search keywords,

Patient

Encrypt

Search Token

Cloud

Result

Upload

Encrypt

Index

SSE

Fig. 3. The First Scenario: Owner as Reader/Writer

(ii) leakage due to search patterns, and (iii) leakage due to

access patterns.

Privacy of search keywords. This is related to plaintext

privacy: if the cloud server deduces any association between

frequent keywords and encrypted dataset from the index,

it may learn the main content of the EHR data. Therefore,

searchable index should be constructed in such a way that

prevents the cloud server from performing such kind of as-

sociation attacks. This kind of security is also called security

against chosen keyword attack (CKA). In an SE scheme, the

randomness of encrypted index guarantees to prevent such

attacks from the cloud server.

Privacy of search patterns. Data users usually prefer

to keep their query from being exposed to others, i.e.,

the keyword indicated by the corresponding token. In the

literature [7], [8], [9], [10], this kind of leakage is called

search pattern. Namely search patterns reveal whether the

same search was performed in the past or not. Accessing

the search pattern allows the server to use statistical analysis

and determine information about the query keywords. Us-

ing deterministic trapdoors directly leaks the search pattern.

Existing randomized SE schemes use randomly generated

tokens to guarantee the privacy of a user’s search pattern.

This security notion of randomized SE schemes is called

predicate privacy [6]. Note that randomizing token generation

algorithm only contributes to defend outside adversaries

of the cloud server but not inner adversaries (e.g., cloud

administrators), because the entry of index touched in

each search process discloses the search pattern as well.

A possible approach to reduce this leakage is to re-order

the keywords and re-generate the index periodically, say

semimonthly or monthly.

Privacy of access patterns. Besides the above privacy

requirements, we note that the sequence of search outcomes

of most SE constructions will likely reveal the information

of the keywords since the cloud server will always return

the same document set for the same queried keyword. This

kind of leakage is referred to as access pattern [7]. Access

pattern refers to the information that is implied by the

query results. Only ORAM can hide access patterns. But

ORAM is computationally intensive and do not scale well

for real world datasets. In practice, one may reduce (but not

eliminate) the leakage of access patterns. For example, one

could randomly insert fake documents in the bitmaps [11].

In this paper, we focus on the techniques for ensuring

plaintext privacy against any adversary and predicate privacy

against outsider adversaries of the cloud server. The above-

mentioned techniques can be combined with the four types

of SE schemes to reduce search and access pattern leakage

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