PriStream: Privacy-preserving distributed stream monitoring of thresholded PERCENTILE statistics

Sun, Jing; Zhang, Rui; Zhang, Jinxue; Zhang, Yanchao

doi:10.1109/infocom.2016.7524461

Cited by 14 publications

(8 citation statements)

References 36 publications

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“…e sensitivity of the mean approximate error is l/size(x), and α x t is the evaluation budget for each group. We can see that the evaluation noise in equation ( 10) has a smaller variance compared to equation (7). However, the mean approximate error also has a shortcoming that the data change on a single page could be ignored.…”

Section: Evaluation and Perturbationmentioning

confidence: 98%

“…Compared with the "counter" algorithm, RTP-DMM achieves better data utility. Sun et al [7] propose the PriStream algorithm to protect the thresholded percentile statistics under event-level privacy. Chen et al [21] propose an event-level privacy model, PeGaSus, to simultaneously support a variety of tasks, such as counts, sliding windows, and event monitoring.…”

Section: Related Workmentioning

confidence: 99%

“…According to their protection range on the time dimension, privacy models are divided into two categories: event-level privacy and user-level privacy, which are represented in Figure 1(a). Event-level privacy [6][7][8] protects the events on different timestamps independently. In contrast, user-level privacy [9,10] protects users' events on all timestamps jointly.…”

Section: Introductionmentioning

confidence: 99%

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Differentially Private Web Browsing Trajectory over Infinite Streams

Liu

Guo

Tan

et al. 2021

Security and Communication Networks

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Nowadays, a lot of data mining applications, such as web traffic analysis and content popularity prediction, leverage users’ web browsing trajectories to improve their performance. However, the disclosure of web browsing trajectory is the most prominent issue. A novel privacy model, named Differential Privacy, is used to rigorously protect user’s privacy. Some works have applied this privacy model to spatial-temporal streams. However, these works either protect the users’ activities in different places separately or protect their activities in all places jointly. The former one cannot protect trajectories that traverse multiple places; while the latter ignores the differences among places and suffers the degradation of data utility (i.e., data accuracy). In this paper, we propose a w , n -differential privacy to protect any spatial-temporal sequence occurring in w successive timestamps and n -range places. To achieve better data utility, we propose two implementation algorithms, named Spatial-Temporal Budget Distribution (STBD) and Spatial-Temporal RescueDP (STR). Theoretical analysis and experimental results show that these two algorithms can achieve a balance between data utility and trajectory privacy guarantee.

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Section: Evaluation and Perturbationmentioning

confidence: 98%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Differentially Private Web Browsing Trajectory over Infinite Streams

Liu

Guo

Tan

et al. 2021

Security and Communication Networks

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“…In crowd-sourced systems, high-dimensional heterogeneous data are ubiquitous. With the increases in data dimensionality and the dimensional differences between different attributes, many existing local differential privacy mechanisms such as RAPPOR [15] and [16], [17], if straightforwardly applied to multiple attributes with unbalanced dimensions, will become extremely unavailable. Their fatal drawbacks are the use of non-optimized privacy budget allocation schemes and their high computational complexities, which lead to great data utility loss and high latency.…”

Section: Introductionmentioning

confidence: 99%

Combinational Randomized Response Mechanism for Unbalanced Multivariate Nominal Attributes

Feng

Zhang

et al. 2020

IEEE Access

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This work was supported by "the Construction team project of the introduction and cultivation of young innovative talents in Colleges and universities of Shandong Province of China, 2019. (Project name: Big data and business intelligence social service innovation team)" ABSTRACT At present, many enterprises provide users with better services by collecting their sensitive information. However, these enterprises will inevitably cause the leakage of users' information, thereby infringing on users' privacy. Local differential privacy resolves this problem by only aggregating randomized values from each user, providing plausible deniability. However, different users might have diverse privacy requirements for different attributes. Moreover, the dimensions of these attributes may be unbalanced. Traditional local differential privacy algorithms usually assign the same privacy budget to all attributes, resulting in undesired frequency estimation. To obtain highly accurate of the results while satisfying local differential privacy, the aggregator needs to implement a reasonable privacy budget allocation scheme. Motivated by this, this paper proposed a novel local differential privacy scheme. The proposed method combines the advantages of BRR and MRR to address the problem of high and low privacy requirements. It employs the Lagrange multiplier algorithm to transform the privacy budget allocation problem between unbalanced attributes into a problem of calculating minima from unconditionally constrained convex functions. The solution to the resulting nonlinear equation is used as the final privacy budget allocation scheme. Simulation experiments show that the novel local differential privacy scheme proposed by this paper can significantly reduce the estimation error under the premise of satisfying the local differential privacy.

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“…Kale et al [17] used the trigger method to study threshold monitoring, proposed five evaluation parameters to measure the performance of the algorithm and provided many potential solutions, such as the statistical probability-based method, the global distributed hash table method, and others. Sun et al [18], to meet the needs of distributed data flow monitoring in smart cities in the future, studied the topic of distributed data flow monitoring from two different perspectives: improving communication efficiency and data privacy protection. In their research, each remote node was assigned multiple local thresholds, representing different “grades”.…”

Section: Introductionmentioning

confidence: 99%

Low-Power Distributed Data Flow Anomaly-Monitoring Technology for Industrial Internet of Things

Han

Tian

Shi

et al. 2019

Sensors

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. In recent years, the industrial use of the internet of things (IoT) has been constantly growing and is now widespread. Wireless sensor networks (WSNs) are a fundamental technology that has enabled such prevalent adoption of IoT in industry. WSNs can connect IoT sensors and monitor the working conditions of such sensors and of the overall environment, as well as detect unexpected system events in a timely and accurate manner. Monitoring large amounts of unstructured data generated by IoT devices and collected by the big-data analytics systems is a challenging task. Furthermore, detecting anomalies within the vast amount of data collected in real time by a centralized monitoring system is an even bigger challenge. In the context of the industrial use of the IoT, solutions for monitoring anomalies in distributed data flow need to be explored. In this paper, a low-power distributed data flow anomaly-monitoring model (LP-DDAM) is proposed to mitigate the communication overhead problem. As the data flow monitoring system is only interested in anomalies, which are rare, and the relationship among objects in terms of the size of their attribute values remains stable within any specific period of time, LP-DDAM integrates multiple objects as a complete set for processing, makes full use of the relationship among the objects, selects only one “representative” object for continuous monitoring, establishes certain constraints to ensure correctness, and reduces communication overheads by maintaining the overheads of constraints in exchange for a reduction in the number of monitored objects. Experiments on real data sets show that LP-DDAM can reduce communication overheads by approximately 70% when compared to an equivalent method that continuously monitors all objects under the same conditions.

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PriStream: Privacy-preserving distributed stream monitoring of thresholded PERCENTILE statistics

Cited by 14 publications

References 36 publications

Differentially Private Web Browsing Trajectory over Infinite Streams

Differentially Private Web Browsing Trajectory over Infinite Streams

Combinational Randomized Response Mechanism for Unbalanced Multivariate Nominal Attributes

Low-Power Distributed Data Flow Anomaly-Monitoring Technology for Industrial Internet of Things

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