Differentially private event sequences over infinite streams

Kellaris, Georgios; Papadopoulos, Stavros; Xiao, Xiaokui; Papadias, Dimitris

doi:10.14778/2732977.2732989

Cited by 194 publications

(201 citation statements)

References 31 publications

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“…Previous work mainly focuses on event-level privacy on finite or infinite streams [14], [16], [17], and user-level privacy on finite streams [5], [7], [15]. Chan et al [16] proposed scheme of p-sum to construct a full binary tree on the sequential data, where each node contains the sum of the sequential data in its subtree, plus noise with scale logarithmic in the length of the sequence.…”

Section: Differential Privacy On Streamsmentioning

confidence: 99%

“…Approximation strategies have been investigated in earlier research, such as histogram publishing [31], [32], and statistics on data stream publishing [5], [14]. Instead of directly adding noise to real data, they function by transformation of original data or a query structure to achieve better overall utility.…”

Section: Approximation Strategiesmentioning

confidence: 99%

“…As explained in Sect. 1, w-event privacy [14] cannot provide personalized and uniform protection. The former is true because essentially w-event privacy is an event-level, not a user-level privacy model.…”

Section: Privacy Evaluationmentioning

confidence: 99%

“…We run a private data publishing algorithm BD [14] of w-event privacy, and calculate the ratio of risky utrajectories. We set w as 10 to 90 with intervals of 20, and u = w. From the result shown in Table 3 (e.g., the first cell of 9.1% is the ratio of risky 10-trajectories under the protection of 10-event privacy), we can conclude that utrajectories cannot be uniformly protected by w-event privacy with a given u and w, unless w u or dense trajectory (data points appear at each cells in Fig.…”

Section: Privacy Evaluationmentioning

confidence: 99%

“…To this end, a flexible privacy model of w-event privacy [14] based on -DP for infinite stream data was proposed recently. Instead of protecting the whole length of stream data, it focuses on protecting the data within any single sliding window.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Differentially Private Real-Time Data Publishing over Infinite Trajectory Streams

Cao

Yoshikawa

2016

IEICE Trans. Inf. & Syst.

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SUMMARYRecent emerging mobile and wearable technologies make it easy to collect personal spatiotemporal data such as activity trajectories in daily life. Publishing real-time statistics over trajectory streams produced by crowds of people is expected to be valuable for both academia and business, answering questions such as "How many people are in Kyoto Station now?" However, analyzing these raw data will entail risks of compromising individual privacy. -Differential Privacy has emerged as a well-known standard for private statistics publishing because of its guarantee of being rigorous and mathematically provable. However, since user trajectories will be generated infinitely, it is difficult to protect every trajectory under -differential privacy. On the other hand, in real life, not all users require the same level of privacy. To this end, we propose a flexible privacy model of -trajectory privacy to ensure every desired length of trajectory under protection of -differential privacy. We also design an algorithmic framework to publish -trajectory private data in real time. Experiments using four real-life datasets show that our proposed algorithms are effective and efficient.

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Section: Differential Privacy On Streamsmentioning

confidence: 99%

Section: Approximation Strategiesmentioning

confidence: 99%

Section: Privacy Evaluationmentioning

confidence: 99%

Section: Privacy Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Differentially Private Real-Time Data Publishing over Infinite Trajectory Streams

Cao

Yoshikawa

2016

IEICE Trans. Inf. & Syst.

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Differentially Private Generative Adversarial Networks for Time Series, Continuous, and Discrete Open Data

Frigerio¹,

Oliveira²,

Gomez³

et al. 2019

ICT Systems Security and Privacy Protection

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Open data plays a fundamental role in the 21th century by stimulating economic growth and by enabling more transparent and inclusive societies. However, it is always difficult to create new high-quality datasets with the required privacy guarantees for many use cases. This paper aims at creating a framework for releasing new open data while protecting the individuality of the users through a strict definition of privacy called differential privacy. Unlike previous work, this paper provides a framework for privacy preserving data publishing that can be easily adapted to different use cases, from the generation of time-series to continuous data, and discrete data; no previous work has focused on the later class. Indeed, many use cases expose discrete data or at least a combination between categorical and numerical values. Thanks to the latest developments in deep learning and generative models, it is now possible to model rich-semantic data maintaining both the original distribution of the features and the correlations between them. The output of this framework is a deep network, namely a generator, able to create new data on demand. We demonstrate the efficiency of our approach on real datasets from the French public administration and classic benchmark datasets.

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