Landmark Privacy: Configurable Differential Privacy Protection for Time Series

Katsomallos, Manos; Tzompanaki, Katerina; Kotzinos, Dimitris

doi:10.1145/3508398.3511501

Cited by 8 publications

(4 citation statements)

References 38 publications

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“…Other privacy-preserving methods proposed for data stream mining use different techniques such as fuzzy logic and PCA (Rajesh et al 2012), differential privacy (Chamikara et al 2019;Katsomallos et al 2022;Gondara et al 2022), sliding window (Lin et al 2016), and hashing (Nyati et al 2018). These methods try to overcome the challenges in data stream mining by using different techniques.…”

Section: Ppdm Methods For Data Stream Miningmentioning

confidence: 99%

Privacy-preserving data (stream) mining techniques and their impact on data mining accuracy: a systematic literature review

2023

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This study investigates existing input privacy-preserving data mining (PPDM) methods and privacy-preserving data stream mining methods (PPDSM), including their strengths and weaknesses. A further analysis was carried out to determine to what extent existing PPDM/PPDSM methods address the trade-off between data mining accuracy and data privacy which is a significant concern in the area. The systematic literature review was conducted using data extracted from 104 primary studies from 5 reputed databases. The scope of the study was defined using three research questions and adequate inclusion and exclusion criteria. According to the results of our study, we divided existing PPDM methods into four categories: perturbation, non-perturbation, secure multi-party computation, and combinations of PPDM methods. These methods have different strengths and weaknesses concerning the accuracy, privacy, time consumption, and more. Data stream mining must face additional challenges such as high volume, high speed, and computational complexity. The techniques proposed for PPDSM are less in number than the PPDM. We categorized PPDSM techniques into three categories (perturbation, non-perturbation, and other). Most PPDM methods can be applied to classification, followed by clustering and association rule mining. It was observed that numerous studies have identified and discussed the accuracy-privacy trade-off. However, there is a lack of studies providing solutions to the issue, especially in PPDSM.

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Section: Ppdm Methods For Data Stream Miningmentioning

confidence: 99%

Privacy-preserving data (stream) mining techniques and their impact on data mining accuracy: a systematic literature review

2023

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“…In the context of the Taxi example, these approaches provide similar privacy protections to a passenger regardless of the proximity to sensitive locations. Landmark privacy [11] makes a move in the direction of utilizing the different characteristics of data streams. It claims that, in reality, not all timestamps and data should be treated equally because some of them may contain significantly more private information or valuable information.…”

Section: Related Workmentioning

confidence: 99%

“…From the related work, we select two typical algorithms of w-event DP, i.e., budget division (BD) and budget absorption (BA), as baseline [9]. Furthermore, since landmark privacy offers a similar privacy guarantee to our proposed PPM, we also compare its performance by evaluating its proposed adaptive algorithm [3]. The privacy budgets of BD, BA, and landmark privacy are converted from their original definitions to the one defined by pattern-level DP.…”

Section: A Experiments Setup 1) Datasetmentioning

confidence: 99%

Differential Privacy for Protecting Private Patterns in Data Streams

He¹,

Plagemann²,

Benndorf³

et al. 2023

2023 IEEE 39th International Conference on Data Engineering Workshops (ICDEW)

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Complex event processing (CEP) is a powerful and increasingly more important tool to analyse data streams for Internet of Things (IoT) applications. These data streams often contain private information that requires proper protection. However, privacy protection in CEP systems is still in its infancy, and most existing privacy-preserving mechanisms (PPMs) are adopted from those designed for data streams. Such approaches undermine the quality of the entire data stream and limit the performance of IoT applications. In this paper, we attempt to break the limitation and establish a new foundation for PPMs of CEP by proposing a novel pattern-level differential privacy (DP) guarantee. We introduce two PPMs that guarantee pattern-level DP. They operate only on data that correlate with private patterns rather than on the entire data stream, leading to higher data quality. One of the PPMs provides adaptive privacy protection and brings more granularity and generalization. We evaluate the performance of the proposed PPMs with two experiments on a real-world dataset and on a synthetic dataset. The results of the experiments indicate that our proposed privacy guarantee and its PPMs can deliver better data quality under equally strong privacy guarantees, compared to multiple well-known PPMs designed for data streams.

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“…Continuous data statistics under centralized differential privacy has been extensively studied, [14][15][16][17][18] while analysing such stream data under local differential privacy protection is still at infancy. There are few methods 8,9,[19][20][21] have been proposed to deal with the stream data but with different settings.…”

Section: Stream Data Aggregation With Ldpmentioning

confidence: 99%

SPoFC: A framework for stream data aggregation with local differential privacy

Yang

Lam

Zhu

et al. 2022

Concurrency and Computation

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Collecting and analysing customers' data plays an essential role in the more intense market competition. It is critical to perform data analysis effectively while ensuring the user's privacy, especially after various privacy regulations are enacted. In this paper, we consider the problem of aggregating the stream data generated from wearable devices in a specific time period in a privacy-preserving manner. Specifically, we adopt the local differential privacy mechanism to provide a strong privacy guarantee for users. One major challenge is that all values of the stream need to be perturbed. The additive noise makes it hard to release an accurate data stream. One way to reduce the noise scale is to select some data points to perturb instead of all. The intuition is that more privacy budgets are applied to a single data point, which ensures the statistical accuracy. The perturbed data points are used to predict the un-selected data points without consuming an extra privacy budget. Based on this idea, we propose a novel stream data statistical framework, which includes four components, data fitting, skeleton point selection, noisy stream generation, and data aggregation. Extensive experiment results show that our proposed method achieves a much smaller mean square error given a fixed privacy budget compared with the state-of-the-art.

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Landmark Privacy: Configurable Differential Privacy Protection for Time Series

Cited by 8 publications

References 38 publications

Privacy-preserving data (stream) mining techniques and their impact on data mining accuracy: a systematic literature review

Privacy-preserving data (stream) mining techniques and their impact on data mining accuracy: a systematic literature review

Differential Privacy for Protecting Private Patterns in Data Streams

SPoFC: A framework for stream data aggregation with local differential privacy

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