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Differential privacy, a cornerstone of privacy-preserving techniques, plays an indispensable role in ensuring the secure handling and sharing of sensitive data analysis across domains such as in census, healthcare, and social networks. Histograms, serving as a visually compelling tool for presenting analytical outcomes, are widely employed in these sectors. Currently, numerous algorithms for publishing histograms under differential privacy have been developed, striving to balance privacy protection with the provision of useful data. Nonetheless, the pivotal challenge concerning the effective enhancement of precision for small bins (those intervals that are narrowly defined or contain a relatively small number of data points) within histograms has yet to receive adequate attention and in-depth investigation from experts. In standard DP histogram publishing, adding noise without regard for bin size can result in small data bins being disproportionately influenced by noise, potentially severely impairing the overall accuracy of the histogram. In response to this challenge, this paper introduces the SReB_GCA sanitization algorithm designed to enhance the accuracy of small bins in DP histograms. The SReB_GCA approach involves sorting the bins from smallest to largest and applying a greedy grouping strategy, with a predefined lower bound on the mean relative error required for a bin to be included in a group. Our theoretical analysis reveals that sorting bins in ascending order prior to grouping effectively prioritizes the accuracy of smaller bins. SReB_GCA ensures strict ϵ -DP compliance and strikes a careful balance between reconstruction error and noise error, thereby not only initially improving the accuracy of small bins but also approximately optimizing the mean relative error of the entire histogram. To validate the efficiency of our proposed SReB_GCA method, we conducted extensive experiments using four diverse datasets, including two real-life datasets and two synthetic ones. The experimental results, quantified by the Kullback–Leibler Divergence (KLD), show that the SReB_GCA algorithm achieves substantial performance enhancement compared to the baseline method (DP_BASE) and several other established approaches for differential privacy histogram publication. [ABSTRACT FROM AUTHOR]

Continuous real-time location data is very important in the big data era, but the privacy issues involved is also a considerable topic. It is not only necessary to protect the location privacy at each release moment, but also have to consider the impact of data correlation. Correlated Laplace Mechanism (CLM) is a sophisticated method to implement differential privacy on correlated time series. This paper aims to solve the key problems of applying CLM in continuous location release. Based on the finding that the location increment is approximately stationary in many scenarios, a location correlation estimation method based on the location increment is proposed to solve the problem of nonstationary location data correlation estimation; an adaptive adjustment model for the CLM filter based on parameter quantization idea (QCLM) as well as its effective implementation named QCLM-Lowpass utilizing the lowpass spectral characteristics of location data series is proposed to solve the problem of output deviations due to the undesired transient response of the CLM filter in time-varying environments. Extensive simulations and real data experiments validate the effectiveness of the proposed approach and show that the privacy scheme based on QCLM-Lowpass can offer a better balance between the ability to resist correlation-based attacks and data availability. [ABSTRACT FROM AUTHOR]

Differential privacy (DP) has become a de facto standard to achieve data privacy. However, the utility of DP solutions with the premise of privacy priority is often unacceptable in real-world applications. In this paper, we propose the best-effort differential privacy (B-DP) to promise the preference for utility first and design two new metrics including the point belief degree and the regional average belief degree to evaluate its privacy from a new perspective of preference for privacy. Therein, the preference for privacy and utility is referred to as expected privacy protection (EPP) and expected data utility (EDU), respectively. We also investigate how to realize B-DP with an existing DP mechanism (KRR) and a newly constructed mechanism (EXP Q ) in the dynamic check-in data collection and publishing. Extensive experiments on two real-world check-in datasets verify the effectiveness of the concept of B-DP. Our newly constructed EXP Q can also satisfy a better B-DP than KRR to provide a good trade-off between privacy and utility. [ABSTRACT FROM AUTHOR]

Privacy preserving methods supporting for data aggregating have attracted the attention of researchers in multidisciplinary fields. Among the advanced methods, differential privacy (DP) has become an influential privacy mechanism owing to its rigorous privacy guarantee and high data utility. But DP has no limitation on the bound of noise, leading to a low-level utility. Recently, researchers investigate how to preserving rigorous privacy guarantee while limiting the relative error to a fixed bound. However, these schemes destroy the statistical properties, including the mean, variance and MSE, which are the foundational elements for data aggregating and analyzing. In this paper, we explore the optimal privacy preserving solution, including novel definitions and implementing mechanisms, to maintain the statistical properties while satisfying DP with a fixed relative error bound. Experimental evaluation demonstrates that our mechanism outperforms current schemes in terms of security and utility for large quantities of queries. [ABSTRACT FROM AUTHOR]

Although data analysis and mining technologies can efficiently provide intelligent and personalized services to us, data owners may not always be willing to share their true data because of privacy concerns. Recently, differential privacy (DP) technology has achieved a good trade-off between data utility and privacy guarantee by publishing noisy outputs. Nonetheless, DP still has a risk of privacy leakage when handling correlated data directly. Current schemes attempt to extend DP to publish correlated data, but are faced with the challenge of violating DP or low-level data utility. In this paper, we try to explore the essential cause of this inapplicability. Specifically, we suppose that this inapplicability is caused by the different correlations between noise and original data. To verify our supposition, we propose the notion of Correlation-Distinguishability Attack (CDA) to separate IID (Independent and Identically Distributed) noise from correlated data. Furthermore, taking time series as an example, we design an optimum filter to realize CDA in practical applications. Experimental results support our supposition and show that, the privacy degree of current approaches has a degradation under CDA. [ABSTRACT FROM AUTHOR]

The rapid development of data sharing applications brings a serious problem of privacy disclosure. As an effective privacy-preserving method, the differential privacy, which strictly defines the privacy-preserving degree and data utility mathematically, can balance the privacy and data utility. However, the differential privacy has a hypothesis premise that the raw data are accurate without any error, so it could not limit the privacy security and the data utility to the expected range when processing data with errors. Hence, this paper focuses on the study on the influence of data errors on differential privacy. Taking the random error as an example, we analyze the influence mode and mechanism of data errors on differential privacy, especially on the privacy budget ε . The theoretical derivations and experimental simulations prove that the Laplace mechanism still preserves ε ′ -indistinguishability for data with errors. Moreover, the random algorithm can realize the expected privacy preserving strength by adding less noise compared with the algorithm that do not consider data errors, and has a better data utility by reducing the unnecessary cost of utility. This paper defines the research directions on the differential privacy theory concerning of data errors, and provides the foundations of perfecting the theory system and promoting the practicality of the differential privacy. [ABSTRACT FROM AUTHOR]