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Among the advanced methods, differential privacy (DP), introducing independent Laplace noise, has become an influential privacy mechanism owing to its provable and rigorous privacy guarantee. Nonetheless, in practice, POI data to be protected is always correlated, while independent noise may cause undesirable information disclosure than expected. Recent researches attempt to optimize the sensitivity function of DP with consideration of the correlation strength between POI—but there is a drawback in a substantial growth of noise level. To remedy this problem, this paper exploits the degradation of DP in expected privacy levels for correlated POI data and proposes a solution to mitigate it. We propose a generalized Laplace mechanism to achieve privacy guarantees. Specifically, we design a practical iteration mechanism, including an update function, to conduct a generalized Laplace mechanism when facing large scale queries. Experimental evaluation on real-world datasets over multiple fields show that our solution consistently outperforms state-of-the-art mechanisms in data utility while providing the same privacy guarantee as other approaches for correlated POI data.

The rapid growth of GPS technology and mobile devices has led to a massive accumulation of location data, bringing considerable benefits to individuals and society. One of the major usages of such data is travel time prediction, a typical service provided by GPS navigation devices and apps. Meanwhile, the constant collection and analysis of the individual location data also pose unprecedented privacy threats. We leverage the notion of geo-indistinguishability, an extension of differential privacy to the location privacy setting, and propose a procedure for privacy-preserving travel time prediction without collecting actual individual GPS trace data. We propose new concepts to examine the impact of geo-indistinguishability-based sanitization on the usefulness of GPS traces and provide analytical and experimental utility analysis for privacy-preserving travel time prediction. We also propose new metrics to measure the adversary error in learning individual GPS traces from the collected sanitized data. Our experiment results suggest that the proposed procedure provides travel time prediction with satisfactory accuracy at reasonably small privacy costs.

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.

The abuse of antibiotics leads to an increasing emergence of drug-resistant bacteria, which not only causes a waste of medical resources but also seriously endangers people’s health and life safety. Therefore, it is highly desirable to develop an efficient antibacterial strategy to reduce the reliance on traditional antibiotics. Antibacterial photodynamic therapy (aPDT) is regarded as an intriguing antimicrobial method that is less likely to generate drug resistance, but its efficiency still needs to be further improved. Herein, a robust titanium-based metal–organic framework ACM-1 was adopted to support Ag nanoparticles (NPs) to obtain Ag NPs@ACM-1 for boosting antibacterial efficiency via synergistic chemical–photodynamic therapy. Apart from the intrinsic antibacterial nature, Ag NPs largely boost ROS production and thus improve aPDT efficacy. As a consequence, Ag NPs@ACM-1 shows excellent antibacterial activity under visible light illumination, and its minimum bactericidal concentrations (MBCs) against E. coli, S. aureus, and MRSA are as low as 39.1, 39.1, and 62.5 μg mL–1, respectively. Moreover, to expand the practicability of Ag NPs@ACM-1, two (a dense and a loose) Ag NPs@ACM-1 films were readily fabricated by simply dispersing Ag NPs@ACM-1 into heated aqueous solutions of edible agar and sequentially cooling through heating or freeze-drying, respectively. Notably, these two films are mechanically flexible and exhibit excellent antibacterial activities, and their antimicrobial performances can be well retained in their recyclable and remade films. As agar is nontoxic, degradable, inexpensive, and ecosustainable, the dense and loose Ag NPs@ACM-1 films are potent to serve as recyclable and degradable antibacterial plastics and antibacterial dressings, respectively.