Trajectory-aware privacy-preserving method with local differential privacy in crowdsourcing

IF 2.5 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Yingcong Hong, Junyi Li, Yaping Lin, Qiao Hu, Xiehua Li
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引用次数: 0

Abstract

In spatial crowdsourcing services, the trajectories of the workers are sent to a central server to provide more personalized services. However, for the honest-but-curious servers, it also poses a challenge in terms of potential privacy leakage of the workers. Local differential privacy (LDP) is currently the latest technique to protect data privacy. However, most of LDP-based schemes have limitations in providing good utility due to extensive noise in perturbing trajectories. In this work, to balance the privacy and utility, we propose a novel pattern-aware privacy protection method called trajectory-aware privacy-preserving with local differential privacy (TALDP). The key idea is that, rather than applying the same degree of perturbation to all location points, we employ adaptive privacy budget allocation, assigning varied privacy budgets to individual location points, thereby mitigating the perturbation’s impact and enhancing overall utility. Meanwhile, to ensure the privacy, we give the different perturbing points to different privacy budgets according to their important degree for the patterns of the trajectories. In particular, we use Karman filter method to select the important location points and decide their privacy budgets. We conduct extensive experiments on three real datasets. The results show that our approach improves the utility over many other current methods while still provide good the privacy protection.
众包中具有局部差分隐私的轨迹感知隐私保护方法
在空间众包服务中,工作者的轨迹会被发送到中央服务器,以提供更加个性化的服务。然而,对于诚实但好奇的服务器来说,这也带来了工人潜在隐私泄露的挑战。本地差分隐私(LDP)是目前保护数据隐私的最新技术。然而,由于扰动轨迹中存在大量噪声,大多数基于 LDP 的方案在提供良好效用方面存在局限性。在这项工作中,为了平衡隐私和效用,我们提出了一种新颖的模式感知隐私保护方法,称为轨迹感知隐私保护与局部差分隐私(TALDP)。其主要思想是,我们不对所有位置点施加相同程度的扰动,而是采用自适应隐私预算分配,为各个位置点分配不同的隐私预算,从而减轻扰动的影响,提高整体效用。同时,为了确保隐私,我们根据不同扰动点对轨迹模式的重要程度,为其分配不同的隐私预算。具体来说,我们使用卡曼滤波法来选择重要的位置点,并决定其隐私预算。我们在三个真实数据集上进行了大量实验。结果表明,我们的方法比许多其他现有方法更实用,同时还能很好地保护隐私。
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来源期刊
EURASIP Journal on Information Security
EURASIP Journal on Information Security COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
8.80
自引率
0.00%
发文量
6
审稿时长
13 weeks
期刊介绍: The overall goal of the EURASIP Journal on Information Security, sponsored by the European Association for Signal Processing (EURASIP), is to bring together researchers and practitioners dealing with the general field of information security, with a particular emphasis on the use of signal processing tools in adversarial environments. As such, it addresses all works whereby security is achieved through a combination of techniques from cryptography, computer security, machine learning and multimedia signal processing. Application domains lie, for example, in secure storage, retrieval and tracking of multimedia data, secure outsourcing of computations, forgery detection of multimedia data, or secure use of biometrics. The journal also welcomes survey papers that give the reader a gentle introduction to one of the topics covered as well as papers that report large-scale experimental evaluations of existing techniques. Pure cryptographic papers are outside the scope of the journal. Topics relevant to the journal include, but are not limited to: • Multimedia security primitives (such digital watermarking, perceptual hashing, multimedia authentictaion) • Steganography and Steganalysis • Fingerprinting and traitor tracing • Joint signal processing and encryption, signal processing in the encrypted domain, applied cryptography • Biometrics (fusion, multimodal biometrics, protocols, security issues) • Digital forensics • Multimedia signal processing approaches tailored towards adversarial environments • Machine learning in adversarial environments • Digital Rights Management • Network security (such as physical layer security, intrusion detection) • Hardware security, Physical Unclonable Functions • Privacy-Enhancing Technologies for multimedia data • Private data analysis, security in outsourced computations, cloud privacy
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