差分隐私技术在多用户位置关联保护中的应用

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"这篇研究论文探讨了在大数据时代，随着基于位置的应用、GPS设备和大数据机构的快速发展，位置关联隐私问题日益引起人们的关注。作者提出了一种利用差分隐私技术来保护多用户位置关联的新方法，旨在防止对手通过结合位置关联和背景知识推测用户的隐私。传统的解决方案如位置扰动和一般化主要依赖于语义隐私模型，但缺乏严格的隐私保障。此外，许多方法仅考虑单个用户的位置扰动，而未考虑多个用户之间的关联保护。" 本文的焦点在于解决多用户位置关联保护的问题，利用差分隐私这一强大的隐私保护工具，为用户提供更安全的地理位置服务。差分隐私是一种数学框架，它允许数据发布者在不泄露任何个体信息的情况下分享统计信息。通过向数据添加噪声，差分隐私可以在提供有用信息的同时，确保任何特定个体的数据贡献难以被识别。论文可能涵盖了以下关键点： 1. **位置关联隐私**：当多个用户的位置数据被收集并分析时，对手可能通过分析这些位置关联来推断出用户的敏感信息，如生活习惯、工作地点或家庭住址。因此，保护位置关联是保护用户隐私的重要一环。 2. **差分隐私原理**：差分隐私通过在用户位置数据上添加随机噪声，使得攻击者无法确定任何特定个体是否参与了数据集。这为位置数据提供了严格的数学隐私保障，即使在大量数据中，也能有效防止个体信息的泄露。 3. **多用户位置关联保护**：与以往只关注单个用户位置保护的方法不同，该研究考虑了多个用户的位置数据，旨在创建一个系统，能够同时保护所有用户的关联性，防止因多个用户的位置信息组合而导致的隐私泄露。 4. **位置扰动与一般化**：虽然这些方法在一定程度上保护了用户位置隐私，但它们通常没有提供严格的隐私保证。论文可能会对比和评估这些传统方法与差分隐私方法的效果和优势。 5. **背景知识与隐私风险**：论文可能会讨论对手如何利用他们的背景知识来增强对位置数据的分析，以及所提出的差分隐私方案如何在这种情况下降低隐私风险。 6. **性能与实用性的权衡**：在增加隐私保护的同时，可能会影响数据的实用性。论文可能会探讨如何在保持数据准确性与提升隐私保护之间找到平衡。 7. **实验与评估**：论文可能包括了对所提方法的实证评估，通过模拟攻击者和比较不同隐私策略的结果，展示新方法的有效性和效率。这篇研究论文为多用户位置关联保护提供了一个新的视角，即利用差分隐私技术，为大数据时代的隐私保护开辟了新的路径。这种方法对于保障用户在享受便捷的地理位置服务时的隐私权益至关重要。

Multi-user Location Correlation Protection with

Differential Privacy

Lu Ou

†

, Zheng Qin

†

, Yonghe Liu

⊤

,HuiYin

†§

, Yupeng Hu

†

, Hao Chen

†

College of Information Science and Engineering, Hunan University, Changsha 410082, China

⊤

Department of Computer Science and Engineering, University of Texas at Arlington, Arlington 76013, USA

Department of Mathematics and Computer Science, Changsha University, Changsha 410022, China

Abstract—In the big data era, with the rapid development of

location-based applications, GPS enabled devices and big data

institutions, location correlation privacy raises more and more

people’s concern. Because adversaries may combine location

correlations with their background knowledge to guess users’

privacy, such correlation should be protected to preserve users’

privacy. In order to deal with the location disclosure problem,

location perturbation and generalization have been proposed.

However, most proposed approaches depend on syntactic privacy

models without rigorous privacy guarantee. Furthermore, many

approaches only consider perturbing the locations of one user

without considering multi-user location correlations, so these

techniques cannot prevent various inference attacks well. Cur-

rently, differential privacy has been regarded as a standard for

privacy protection, but there are new challenges for applying

differential privacy in the location correlations protection. The

privacy protection not only should meet the needs of users who

request location-based services, but also should protect location

correlation among multiple users.

In this paper, we propose a systematic solution to protect

location correlations privacy among multiple users with rigorous

privacy guarantee. First of all, we propose a novel deﬁnition,

private candidate sets which are obtained by hidden Markov

models. Then, we quantify the location correlation between two

users by using the similarity of hidden Markov models. Finally,

we present a private trajectory releasing mechanism which can

preserve the location correlations among users who move under

hidden Markov models in a period of time. Experiments on

real-world datasets also show that multi-user location correlation

protection is efﬁcient.

Keywords—differential privacy, hidden Markov models,

location-based services, location correlation, the similarity of

hidden Markov models, private trajectory releasing

I. INTRODUCTION

In the big data era, with the popularity of smart phones

and other GPS enabled devices, the Location Based Service

(LBS) is an integral part of our life. This service can bring

convenience in our life. Therefore, in order to improve quality

of our life, big data institutions may improve the quality of

LBSs. At the same time, big data institutions will achieve

location information about each user and other related infor-

mation from location-based servers such as trajectories and the

user’s interest to improve the quality of LBSs. However, the

big data institutions are honest and curious. They may mine

some sensitive information about users, such as a location

correlation. For instance, there is a big data institution of

catering who wants to analyze restaurant interests for different

occupational groups. Through data mining, they may ﬁnd that

users who are in the same occupational group are often at

the same location at the same time or the different time (as

shown in Figure 1). Furthermore, they may use this location

correlation to mine social correlations among the users. If

social correlations among multiple users are exposed, some

sensitive information may be inferred. Now we give a scenario

to explain this problem. As we can see in Figure 1, Betty

and Jerry are students (i.e., they are in the same occupational

group). Simultaneously, they are at same location (i.e., the

location correlation between Betty and Jerry) twice. Therefore,

adversaries may consider that Jerry and Betty are classmates

according to their background knowledge. Then if Jerry’s age

is leaked, Betty’s age will be known by adversaries too. Thus,

location correlation should be hidden to preserve the user’s

privacy.

Jerry

Betty

Mia

Andy

Student

Doctor

Fig. 1. User Location Trace about Different Occupational Groups

There are several works on location privacy protection.

But most of these existing approaches and solutions focus on

a single user’s privacy. Although a few works also consider

temporal correlations of one user, no location correlations

among multiple users.

To protect location correlations privacy among multiple

users with rigorous privacy guarantee, we propose a novel pri-

vate trajectory releasing mechanism which is based on hidden

Markov models (HMMs) to deal with this location correlation

disclosure problem. By using HMMs, a probable trajectory

set can be generated to cover every eventuality (trajectory).

And by using a similarity of HMMs, we can quantify the

locations correlation between two users in a period of time.

Then, based on the probable trajectories and the correlated

trajectories, we calculate a private candidate set which is used

to achieve differential privacy. Finally, an extended differential

privacy approach is proposed to protect the privacy.

We implement our approach on real-world datasets, and

the experimental results show that our approach could protect

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