WF-LDPSR: A local differential privacy mechanism based on water-filling for secure release of trajectory statistics data

IF 4.8 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Yan-zi Li , Li Xu , Jing Zhang , Liao-ru-xing Zhang
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引用次数: 0

Abstract

Open Data Processing Services are used to solve the bottleneck of big data storage and operation. At the same time, massive trajectory data is generated, and the basic information of users’ spatio-temporal historical data is provided, including points of interest and movement patterns. Improving the availability of published trajectory statistics data without compromising user privacy is critical. Differential privacy technology is a standard technology to realize the secure release of trajectory statistics data. Several research efforts have focused on secure publication of trajectory statistics data in a central environment by adding noise to a trusted third-party server. However, this central approach is vulnerable to privacy breaches, where adversaries can access real data by locking down the third-party server. The local differential privacy, based on a distributed architecture, overcomes this form of attack by allowing users to scramble personal data records before they are sent to third-party server. However, the existing distributed privacy protection schemes still have the balance problem of poor availability of data when ensuring privacy, as well as the problem of excessive operation cost. Therefore, a local differential privacy mechanism based on water-filling for secure release of trajectory statistics data (WF-LDPSR) is proposed in this paper. Firstly, in order to protect user privacy individually, a user automatic personalized segmentation method is proposed to determine the effective user sensitivity level automatically. Secondly, a distributed privacy protection model based on local differential privacy is designed to resist the attacks on the third-party server. Finally, in order to achieve the optimal allocation of privacy budget, the water-filling theorem in the field of communication is introduced. An adaptive privacy budget allocation algorithm based on water-filling theorem is proposed to realize the adaptive privacy budget allocation. In addition, to further improve data availability, a group processing idea based on user set sampling is proposed, which divides users into multiple disjoint subsets randomly, thus reducing the differential privacy noise effectively. Experiments prove that compared with other advanced mechanisms, the WF-LDPSR mechanism can improve the availability of published data by 84.92% while protecting user privacy.
WF-LDPSR:基于注水的局部差分隐私机制,用于安全发布轨迹统计数据
利用开放数据处理服务,解决大数据存储和运营的瓶颈。同时,生成海量轨迹数据,提供用户时空历史数据的基本信息,包括兴趣点和运动模式。在不损害用户隐私的前提下提高已发布轨迹统计数据的可用性至关重要。差分隐私技术是实现轨迹统计数据安全发布的标准技术。一些研究工作侧重于通过向可信第三方服务器添加噪声,在中央环境中安全发布轨迹统计数据。然而,这种中央方法很容易受到隐私泄露的影响,对手可以通过锁定第三方服务器来获取真实数据。基于分布式架构的本地差分隐私保护克服了这种形式的攻击,允许用户在个人数据记录发送到第三方服务器之前对其进行加扰处理。然而,现有的分布式隐私保护方案仍然存在确保隐私时数据可用性差的平衡问题,以及操作成本过高的问题。因此,本文提出了一种基于注水的轨迹统计数据安全发布局部差分隐私机制(WF-LDPSR)。首先,为了单独保护用户隐私,提出了一种用户自动个性化分割方法,自动确定有效的用户敏感度等级。其次,设计了一种基于局部差分隐私的分布式隐私保护模型,以抵御对第三方服务器的攻击。最后,为了实现隐私预算的最优分配,引入了通信领域的注水定理。提出了基于注水定理的自适应隐私预算分配算法,以实现自适应隐私预算分配。此外,为了进一步提高数据可用性,还提出了基于用户集抽样的分组处理思想,将用户随机分为多个不相邻的子集,从而有效降低了差异隐私噪声。实验证明,与其他先进机制相比,WF-LDPSR 机制在保护用户隐私的同时,能将发布数据的可用性提高 84.92%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Computers & Security
Computers & Security 工程技术-计算机:信息系统
CiteScore
12.40
自引率
7.10%
发文量
365
审稿时长
10.7 months
期刊介绍: Computers & Security is the most respected technical journal in the IT security field. With its high-profile editorial board and informative regular features and columns, the journal is essential reading for IT security professionals around the world. Computers & Security provides you with a unique blend of leading edge research and sound practical management advice. It is aimed at the professional involved with computer security, audit, control and data integrity in all sectors - industry, commerce and academia. Recognized worldwide as THE primary source of reference for applied research and technical expertise it is your first step to fully secure systems.
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