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摘要—在无线人体区域传感器网络（WBAN）中，生物传感器被植入或穿戴在个人身上以获取用于临床诊断或物理监测的医学数据。传统上，身份验证和加密技术是用于实现WBAN系统的数据机密性和隐私性。但是，对于WBAN系统，除了数据本身之外，数据传输的模式还可能泄漏有关用户的关键信息。例如，各种医疗传感器以不同的模式传递数据，这些模式泄漏了许多关键事实，例如，使用了什么传感器（这意味着个人可能患有疾病）以及传感器采样的频率（这意味着某些异常观察的紧急程度）关于一个病人）。在这项工作中，为了应对这一挑战，我们设计了一个调节器，该调节器将实际数据会话打包到传输层中与类型无关的传输模型中。所有有效数据包均以调节器定义的频率和相同长度平等发送，以使有效数据传输和传输的固有速度变得混乱。其他参数。我们还提出了一种策略PAS，以最大程度地减少开销，同时防止攻击者找到患者所在的位置。我们广泛的实验验证了我们的调节器和PAS设计。 ### 无线人体传感器网络的模式调节器 #### 摘要随着生物计量传感器和移动计算技术的发展，一种称为无线人体区域传感器网络（Wireless Body Area Network, WBAN）的可穿戴医疗保健系统已被广泛应用于从个体收集并传输医学数据至数据中心。WBAN在疾病诊断与治疗中扮演着辅助角色，尤其是在慢性病患者或老年人群中，能够提升实时数据采集效率、降低成本及减少医院就诊次数。然而，在保障数据保密性和隐私性的传统方法如身份验证与加密技术之外，WBAN中的数据传输模式同样可能泄露关于用户的敏感信息。为解决这一问题，本文设计了一种模式调节器，该调节器能够在传输层将真实数据会话封装进一种类型无关的传输模型中，并通过统一的数据包频率与长度来混淆实际数据传输的固有节奏及其他参数。此外，还提出了一种策略（Pattern Anonymization Strategy, PAS），旨在最小化开销的同时阻止攻击者定位患者位置。 #### 引言随着生物计量传感器技术和移动计算技术的进步，WBAN作为一种能够监测与记录个人生理数据并通过无线通信方式将数据传输至服务器的可穿戴健康护理系统，已经被广泛应用。此类数据在疾病的诊断与治疗过程中起着至关重要的辅助作用。特别是对慢性病患者和老年人来说，WBAN能够显著提高实时数据收集效率，减少医疗成本和医院就诊次数。近年来，许多关于WBAN在医疗保健领域的创新方法也相继问世。然而，在WBAN系统中，除了数据本身之外，数据传输模式也可能泄露关键信息。例如，不同类型的医疗传感器会以不同模式发送数据，这些模式可能会暴露传感器的种类（这可能暗示患者患有某种疾病）以及传感器的采样频率（这反映了某个异常观察结果的紧急程度）。面对这些挑战，研究团队设计了一种模式调节器来解决这些问题。该调节器通过将实际数据会话封装到类型无关的传输模型中，确保所有有效数据包都按照预定义的频率和平等的长度进行传输，从而混淆了实际数据传输的速度和其他参数。此外，还提出了一种名为PAS（Pattern Anonymization Strategy）的策略，旨在最小化开销的同时防止攻击者确定患者的地理位置。 #### 关键知识点详解 **1. 无线人体区域传感器网络（WBAN）：** - **定义**：WBAN是一种由可植入或可穿戴在个人身上的生物传感器组成的网络，用于收集医学数据以进行临床诊断或物理监测。 - **应用场景**：广泛应用于远程健康监护、运动训练监测、军事应用等多个领域。 - **优势**：能够提供连续的生理参数监控，提高诊断准确性，降低医疗成本。 **2. 数据传输模式的隐私风险：** - **传输模式**：指传感器数据的传输频率、数据包大小、传输时间间隔等特征。 - **隐私泄露**：不同类型的医疗传感器以特定的模式传输数据，攻击者可以通过分析这些模式推断出患者的健康状况、活动习惯等个人信息。 - **案例分析**：如果一个特定频率的信号持续出现，攻击者可能猜测到患者正在使用某类特定的医疗传感器，进而推测其健康状况。 **3. 模式调节器的设计：** - **目标**：通过改变数据传输模式来保护患者隐私。 - **实现方式**：将实际数据会话封装到一种类型无关的传输模型中，并确保所有有效数据包都以相同的频率和平等的长度发送，以此混淆实际数据传输的固有速度和其他参数。 - **优势**：有效避免了通过分析数据传输模式而泄露用户隐私的风险。 **4. Pattern Anonymization Strategy (PAS) 策略：** - **目的**：最小化额外开销，同时防止攻击者定位患者的具体位置。 - **实现原理**：通过对传输的数据进行适当的处理，如添加噪声、调整数据包的大小等，来掩盖患者的地理位置信息。 - **有效性验证**：通过广泛的实验验证了模式调节器和PAS策略的有效性。 #### 结论无线人体区域传感器网络（WBAN）为医疗保健领域带来了革命性的变化，但在数据传输模式方面仍存在隐私泄露的风险。为了解决这一问题，研究人员设计了一种模式调节器，它通过改变数据传输模式来保护患者隐私。此外，还提出了一种名为PAS的策略，旨在最小化开销的同时防止攻击者确定患者的地理位置。这些研究成果不仅有助于提高WBAN的安全性和隐私保护水平，也为未来WBAN的应用提供了重要的参考价值。

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Pattern Regulator for Wireless Body Sensor

Networks

Lan Yao

Northeastern University

Shenyang, China 110819

Email: yaolan@mail.neu.edu.cn

Fuxiang Gao

Northeastern University

Shenyang, China 110819

Email: gaofuxiang@mail.neu.edu.cn

Ge Yu

Northeastern University

Shenyang, China 110819

Email: yuge@mail.neu.edu.cn

Abstract—In a wireless body area sensor network (WBAN),

biosensors are implanted or worn on an individual to acquire

medical data for a clinical diagnosis or physical monitoring.

Traditionally, authentication and cryptography techniques are

used to achieve the data conﬁdentiality and privacy of a WBAN

system. However, for WBAN systems, besides data itself, the

pattern of data transmission may leak critical information about

the user. For example, various medical sensors deliver data in

different patterns and these patterns leak many critical facts, e.g.,

what sensors are used (which implies diseases the individual may

have), and how frequent the sensor is sampling (which implies

how urgent some abnormal observation is about a patient). In

this work, to address this challenge, we design a regulator, which

packs real data session into a type-independent transmission

model at transmission layer. All valid data packets are equably

sent at the frequency deﬁned by the regulator and at the same

length to clutter the inherent pace of valid data transmission and

other parameters. We also propose a strategy PAS to minimize the

overhead while preventing attackers from locating the patients.

Our extensive experiments validate our regulator and PAS design.

Index Terms—Wireless Body Networks; pattern detection;

regulator; ML

I. INTRODUCTION

With the evolution of biometrical sensor and mobile com-

puting, a wearable healthcare system which is called Wireless

Body Area Network (WBAN) has been used to monitor and

record medical data from an individual and transmit them

through wireless communication to a data server. This data

plays a signiﬁcant role as an auxiliary for diagnosis or treat-

ment of diseases. Especially for chronic patients or the elder,

WBAN has potential of enhancing real-time data collection,

reducing cost and decreasing hospital visiting times [22]. In

past few years, some innovative methods about WBAN for

healthcare have been developed [4], [7], [11], [25]. They serve

as important parts to monitor cardiac, EEG, heart-rate, SPO2

or respiration data from a patient for further diagnosis or

healthcare. Other extra physical data such as acceleration, gyro

and elevation coulbe also be collected through WBAN for

posture and activity monitoring, which is been used for tumble

alarm and prediction. Another application is electric-training

for athletics [6].

All these systems provide us preliminary applications for

physical monitoring, which must meet increasingly stringent

security (e.g., conﬁdentiality) and privacy requirements for

medical data and patients. Recently, many novel approaches

[1], [8], [19], [24], [27], [29] have been proposed to address

various security and privacy challenges in wireless body area

sensor networks. A wireless body area sensor network is

vulnerable to various attacks due to many limitations of

devices and some unique features of usage of medical sensors,

for example, (1) uncontrolled wireless communication envi-

ronment; (2) limited computing, storage and energy resource

of light-weight medical sensor nodes; (3) dynamic but often

ﬁxed duty cycles of using medical sensors; and (4) dynamic

but often predictable data packet sizes, transmission frequency,

and data values for medical sensors. For example, based

on the speciﬁcation [15], a speciﬁc medical sensor will

report its collected value in a predeﬁned duty cycle pattern

and each data packet will often have a pre-deﬁned length.

Various attacks (both active and passive attacks) may happen

in data sensing, routing, data storage and management. For

example, an illegimate wireless sink will launch a Selective

Forwarding or Worm-hole attacks; sensor nodes exhaust their

energy by receiving unauthenticated packets from attackers

[13]; unveriﬁed data or forged data will lead a chaos to medical

records ; a false relay could modify the medical data and then

forward it to the server [21]. These attacks may be prevented

by carefully designing and deploying data encryption and

authentication methods [16].

In addition to these active attacks, WBAN is susceptible

to more passive attacks than ordinary sensor networks, e.g.,

sometimes a simple eavesdropping attack will reveal the data

or the pattern of data collection by the sensor nodes, which

in turn could breach the security and privacy of WBAN

applications. In this work we focus on a not-well-studied

problem: how to prevent a passive attackers from inferring

some critical information about the patients by eavesdropping

the communication. Notice that active attacks often will either

inject false data, or modify the data, or intercept the data

communicated between the sensor nodes and the receiving sink

node. On the other hand, a passive attacker, by eavesdropping

the communication, could track the signals from medical

sensor and then locate the source even the data is encrypted;

or learn the fact that an individual is using a health monitoring

system; or ﬁnd out the exact types of medical sensors used by

the patient; or analyze the duty-cycle pattern of data collection;

or acquire partial data by studying some correlation among the

2013 IEEE International Conference on High Performance Computing and Communications & 2013 IEEE International Conference

on Embedded and Ubiquitous Computing

DOI 10.1109/HPCC.and.EUC.2013.219

1558

2013 IEEE International Conference on High Performance Computing and Communications & 2013 IEEE International Conference

on Embedded and Ubiquitous Computing

DOI 10.1109/HPCC.and.EUC.2013.219

1558

usage pattern of various sensors.

For example, as different medical sensors will often have

different sensing duty cycles and data packet sizes, a passive

attacker could infer which medical sensors are being used by

simply estimating the duty cycles and sizes of communicated

packets. An attacker could also approximately locate the

patients among a group of people by analyzing spatial variance

of the signal stregnth and applying location techniques [2],

[31].

Although surprisingly powerful and destructive, these pas-

sive attacks (even when the data has been encrypted and au-

thenticated) received little attentions in the literature. Gollakota

et al. [8] and Sorber et al. [27] introduced secure communica-

tion components to achieve data privacy and design schemes to

prevent intruders from data ﬁlching and eavesdropping during

wireless communication between sensor nodes and the sink. In

[27], Sorber et al. assumed that a mobile phone is developed

as a sink for WBAN. A plug-in smart card in a mobile phone

will sear and unseal data to identify any injected incorrect data

or past data and provide a trusted computation environment

so that the eavesdropper will not decode the packets. The

questions addressed in [8] may be the most related one.

Gollakota et al. designed a novel system solution that will

prevent the attacker from receiving any signal from the medical

sensors. Their solution relies on an extra device, called shield,

to jam the direct communication between an IMD (Implanted

Medical Device) and an attacker. The shield (to which the

legitmate sink node is connected) sends jamming signal which

makes it difﬁcult for adversaries to decode. For inalterability of

IMD, this solution makes no modiﬁcation to IMDs themselves

and introduces an extra hardware instead.

These solutions [8], [27] prevent data or signal itself from

being decoded by the eavesdroppers to achieve the privacy.

In this work, we explore the feasibility of shielding in-

dividual medical data from pattern detection without using

extra device. We assume that the data from sensors are

already encrypted for conﬁdentiality, and the eavesdropper

intends to analyse transmission frequency, packet length, RSSI

value and range of signals thereby to discover some medical

monitoring facts of the patient. In this work, we present a

regulator in a medical sensor node. This regulator (some

middle-ware) packs original medical data packet transmission

model into a promiscuous one, so that the transmission pattern

between a sensor and the sink is shielded from attacker. The

adversary who is overhearing the channel only gets features

of the regulator instead of those of real data signals. These

features are independent over inherent pattern of medical data

transmission.

To make the design of regulator practical, we need to

address several key challenges:

• Design a cell-packet which is application-independent

and original medical data packets could be converted to.

The structure of a cell-packet should be appropriate for

both IEEE802.15.4 standard and medical data fragmen-

tation;

• Design a transmission pace to hide the nature of cyclicity

of medical data transmission, as well as it should be

dynamically adjusted to survive the continuously eaves-

dropping;

• It is feasible for a strong adversary to detect patient’s

location through RSSI eavesdropping and analysis. An

energy modulation scheme should be designed to resist

it and extend the lifetime of medical nodes.

To overcome these challenges, ﬁrstly, we deﬁne what a

transmission pattern consist of and how an intruder acts when

eavesdrops, which is discussed in Section II. Secondly, we

must regulate and adjust the transmission in sensor nodes

to shield original patterns, so that the eavesdropper gets

no knowledge from these adjusted signals. We design this

regulator in Section III. To minimize the passive effects of this

adjustment on power consumption, delivery radio, and location

detection, a transformation strategy (PAS ) is proposed in this

section too and the performance of PAS based on medical

data traits is analyzed. We implement the regulator and PAS

in TelosB and Contiki platforms and the implementation and

evaluation results are presented in Section IV.

II. P

RELIMINARY AND SYSTEM MODEL

We ﬁrst brieﬂy present the architecture of a wireless body

area sensor network, the communication model and the passive

attacker model where our regulator design will be based on.

A. WBAN Architecture

Figure 1 illustrates a typical architecture of a WBAN.

In a WBAN, all personalized medical sensors which are

Internet

Database and

Client Portal

Mobile customer

Professional

WBAN for

Individual

WBAN

Backbone Wireless

Network

Sink

Gateway

Client Group

Data Management and Application

Fig. 1: A typical architecture of a WBAN

implanted or worn communicate with the sink through one-

hop transmission directly. Roam occurs when a medical senor

moves beyond transmission range of a sink to that of another.

A local AP is an aggregation point for biological data. An

additional vital task for a sink is to verify the data source. Only

those data from authenticated sensor nodes on individuals will

be accepted and relayed. We assume that cryptography based

techniques have already be in place for ensuring the data

conﬁdentiality, integrity, and authentication.

In the rest of this paper, we will use the following deﬁnition

for the objects presented in Figure 1: individual as a user who

is equipped with medical sensors on/in his body; sink as a

local AP that communicates with sensors directly; gateway

as a remote forwarding router between a sink and Internet

(sometimes gateway and sink are aggregated in one physical

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