隐私保护的压缩感知在众包轨迹恢复中的应用
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"这篇研究论文探讨了在基于群众感应的轨迹恢复中，如何利用隐私保护的压缩感知技术来处理和保护用户的位置数据。随着位置服务的快速发展，从单个位置信息到整个轨迹的利用，由于硬件和能源限制，轨迹数据往往存在大量缺失。为了准确恢复完整的轨迹，论文提出了利用多用户轨迹之间的相关性和先进的压缩感知技术，这种方法在准确性上优于传统的插值方法。然而，轨迹数据暴露了用户的日常活动，因此在群众感应中，隐私问题是一个重大关切。现有的解决方案尽管提供了一定程度的保护，但仍然存在潜在的风险和挑战。"
在本文中，作者 Linghe Kong、Liang He、Xiao-Yang Liu、Yu Gu、Min-You Wu 和 Xue Liu 分别来自麦吉尔大学、密歇根大学、上海交通大学和IBM Research-Austin，他们共同研究了如何在保障用户隐私的同时，有效地进行轨迹恢复。
1. **位置服务与轨迹数据**：随着移动设备的普及，位置服务已经从获取单个位置信息发展到收集和分析用户的完整轨迹。这些轨迹数据对于提供个性化服务和城市规划等有着巨大的价值，但同时也带来了大量的数据处理和存储挑战。
2. **硬件与能源约束**：由于设备的限制，尤其是在大规模群众感应系统中，往往无法持续收集到完整的轨迹数据，这导致数据缺失，影响了轨迹的精确恢复。
3. **压缩感知技术**：为了解决这个问题，论文引入了压缩感知（Compressive Sensing, CS）这一理论。压缩感知是一种信号处理技术，能够在不完全采样信号的情况下重构高维信号，大大减少了数据量，这对于处理轨迹数据非常有效。
4. **轨迹恢复**：通过分析多个用户间的轨迹相关性，结合压缩感知，可以更准确地重建缺失的轨迹部分。相比于传统插值方法，这种方法在处理大规模数据时提高了恢复的精度。
5. **隐私问题**：然而，用户轨迹数据的敏感性使得隐私保护成为必须考虑的因素。暴露的轨迹可能揭示用户的行踪、生活习惯甚至个人信息，对用户隐私构成了威胁。
6. **现有解决方案的局限性**：尽管已有方案试图通过匿名化、加密等手段保护用户隐私，但在实际应用中，这些方法可能被攻击者破解，从而暴露用户信息。
7. **论文贡献**：本文的重点在于探讨如何在保证轨迹恢复效果的同时，增强隐私保护措施，以应对当前解决方案的不足。这可能涉及到开发新的加密算法、设计更安全的数据共享机制，或者利用特定的隐私保护技术与压缩感知相结合。
这篇研究论文旨在解决群众感应中轨迹恢复的准确性与隐私保护的双重挑战，为未来的位置服务和大数据分析提供更加安全且高效的方法。通过深入理解并应用这些技术和理论，可以在保护用户隐私的前提下，提升轨迹数据的利用价值。
Privacy-Preserving Compressive Sensing for
Crowdsensing based Trajectory Recovery
Linghe Kong
∗‡
,LiangHe
†
, Xiao-Yang Liu
‡
,YuGu
§
, Min-You Wu
‡
,XueLiu
∗
∗
McGill University, Canada, Email: linghe.kong@mail.mcgill.ca, xueliu@cs.mcgill.ca
†
University of Michigan, USA, Email: lianghe.umich@gmail.com
‡
Shanghai Jiao Tong University, China, Email: {linghe.kong, yanglet, m wu}@sjtu.edu.cn
§
IBM Research - Austin, USA, Email: yugu@us.ibm.com
Abstract—Location based services ha ve experienced an explo-
sive growth and evolved from utilizing a single location to the
whole trajectory. Due to the hardware and energy constraints,
there are usually many missing data within a trajectory. In order
to accurately recover the complete trajectory, crowdsensing pro-
vides a promising method. This method resorts to the correlation
among multiple users’ trajectories and the advanced compressive
sensing technique, which significantly outperforms conventional
interpolation methods on accuracy. However, as trajectories
exposes users’ daily activities, the privacy issue is a major
concern in crowdsensing. While existing solutions independently
tackle the accurate trajectory recovery and privacy issues, yet
no single design is able to address these two challenges simul-
taneously. Therefore in this paper, we propose a novel Privacy
Preserving Compressive Sensing (PPCS) scheme, which encrypts
a trajectory with several other trajectories while maintaining
the homomorphic obfuscation property for compressive sensing.
Under PPCS, adversaries can only capture the encrypted data,
so the user privacy is preserved. Furthermore, the homomorphic
obfuscation property guarantees that the recovery accuracy of
PPCS is comparable to the state-of-the-art compressive sensing
design. Based on two publicly available traces with numerous
users and long durations, we conduct extensive simulations to
evaluate PPCS. The results demonstrate that PPCS achieves a
high accuracy of <53 m and a large distortion between the
encrypted and the original trajectories (a commonly adopted
metric of privacy strength) of >9,000 m even when up to 50%
original data are missing.
I. INTRODUCTION
Location based services (LBSs) [18, 37] have experienced
an explosive growth recently, which are evolving from utilizing
a single location [7] to harness the complete trajectory of a
mobile user [23, 26, 40]. For example, the Moves application,
which automatically tracks both activities and trajectories of
users, has been downloaded over 4 million times sinc e its
launch in Jan. 2013 and has been acquired by Facebook [1].
Although GPS is universally available on modern devices,
the trajectory of a mobile user may always be incomplete due
to none-line-of-sight to satellites [29]. In addition, since GPS
consumes a significant amount of energy, it is only activated
periodically to conserve energy [22]. Consequently, the trajec-
tory recovery [30] is one of the fundamental components of
LBSs to estimate the missing data in a incomplete trajectory.
For instance, tripperm ap [4] in Flickr can automatically repro-
duce a user’s travelling path based on her geotagged photos.
Considerable interpolation methods have been devoted to
trajectory recovery. With a single user’s incomplete trajectory
data, the methods such as nearest neighbors [31] and linear
interpolation [34] can attain only coarse-grained accuracy.
More recently, Rallapalli et al. [29] reveal that the trajectories
of multiple users within the same geographic area are strongly
correlated. For instance, students in the same campus have
similar time tables; vehicles in the same segment of freeway
moves with similar velocities. Leveraging such correlation s,
the crowdsensing technology [10, 11, 16, 17, 28, 35] provides
a promising recovery method, which collectively recovers all
users’ trajectories together using compressive sensing (CS).
This crowdsensing recovery method is proved to be superior
to interpolation methods with only single user data [29].
While the crowdsensing recovery method accomplishes the
better accuracy, the major drawback for applying it in practice
is its requirement to collect all users’ location data, which
poses great concerns for potential privacy leakage [32, 36].
Especially in crowdsensing, the users are willing to contribute
their personal data only when their privacies are preserved.
Currently, the most commonly adopted privacy-preserving ap-
proach is anonymization [24]. Nevertheless, latest studies [12,
38] reveal that the anonymization mechanism alone is inad e-
quate. To further improve the privacy, dummification [19] and
obfuscation [13, 15, 27] methods are introduced, which inject
fake trajectories and perturb original trajectories, respectively.
Although dummification and obfuscation methods reasonably
protect user privacies, they also pollute the original data, which
decreases the recovery accuracy with current crowdsensing
recovery method.
To tackle the challenges of accurate trajectory recovery
and privacy-preserving simultaneously, we design a novel
encryption method named K-vector perturbation (KVP) to
attain both objectives. The main idea of KVP is to use a private
key to perturb a user’s trajectory with K other trajectories
while maintaining the homomorphic obfuscation property for
compressive sensing. Based on KVP, we propose the privacy-
preserving compressive sensing (PPCS) scheme including
three major steps. First, every user encryp ts her incomplete
location data b y KVP and transmits the d ata in encrypted form
to the crowdsensing server. Second, the server does not need to
decrypt the data but directly recovers all users’ encrypted data
together with CS. Third, a user downloads her corresponding
recovered data and decrypts her own trajectory by inverse
KVP. Under PPCS, adversaries are possible to capture the
2015 IEEE 35th International Conference on Distributed Computing Systems
1063-6927/15 $31.00 © 2015 IEEE
DOI 10.1109/ICDCS.2015.12
31
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