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Metric Differential Privacy (mDP) extends the concept of Differential Privacy (DP) to serve as a new paradigm of data perturbation. It is designed to protect secret data represented in general metric space, such as text data encoded as word embeddings or geo-location data on the road network or grid maps. To derive an optimal data perturbation mechanism under mDP, a widely used method is linear programming (LP), which, however, might suffer from a polynomial explosion of decision variables, rendering it impractical in large-scale mDP. In this paper, our objective is to develop a new computation framework to enhance the scalability of the LP-based mDP. Considering the connections established by the mDP constraints among the secret records, we partition the original secret dataset into various subsets. Building upon the partition, we reformulate the LP problem for mDP and solve it via Benders Decomposition, which is composed of two stages: (1) a master program to manage the perturbation calculation across subsets, and (2) a set of subproblems, each managing the perturbation derivation within a subset. Our experimental results on multiple datasets, including geo-location data in the road network/grid maps, text data, and synthetic data, underscore our proposed mechanism’s superior scalability and efficiency.

 

Geo-obfuscation is a location privacy protection mechanism used by mobile users to conceal their precise locations when reporting location data, and it has been widely used to protect the location privacy of workers in spatial crowdsourcing (SC). However, this technique introduces inaccuracies in the reported locations, raising the question of how to control the quality loss that results from obfuscation in SC services. Prior studies have addressed this issue in time-insensitive SC settings, where some degree of quality degradation can be accepted and the locations can be expressed with less precision, which, however, is inadequate for time-sensitive SC. In this paper, we aim to minimize the quality loss caused by geo-obfuscation in time-sensitive SC applications. To this end, we model workers’ mobility on a fine-grained location field and constrain each worker’s obfuscation range to a set of peer locations, which have similar traveling costs to the destination as the actual location. We apply a linear programming (LP) framework to minimize the quality loss while satisfying both peer location constraints and geo-indistinguishability, a location privacy criterion extended from differential privacy. By leveraging the constraint features of the formulated LP, we enhance the time efficiency of solving LP through the geo-indistinguishability constraint reduction and the column generation algorithm. Using both simulation and real-world experiments, we demonstrate that our approach can reduce the quality loss of SC applications while protecting workers’ location privacy. 

Thiolutin is a natural product transcription inhibitor with an unresolved mode of action. Thiolutin and the related dithiolopyrrolone holomycin chelate Zn2+ and previous studies have concluded that RNA Polymerase II (Pol II) inhibition in vivo is indirect. Here, we present chemicogenetic and biochemical approaches to investigate thiolutin's mode of action in Saccharomyces cerevisiae. We identify mutants that alter sensitivity to thiolutin. We provide genetic evidence that thiolutin causes oxidation of thioredoxins in vivo and that thiolutin both induces oxidative stress and interacts functionally with multiple metals including Mn2+ and Cu2+, and not just Zn2+. Finally, we show direct inhibition of RNA polymerase II (Pol II) transcription initiation by thiolutin in vitro in support of classical studies that thiolutin can directly inhibit transcription in vitro. Inhibition requires both Mn2+ and appropriate reduction of thiolutin as excess DTT abrogates its effects. Pause prone, defective elongation can be observed in vitro if inhibition is bypassed. Thiolutin effects on Pol II occupancy in vivo are widespread but major effects are consistent with prior observations for Tor pathway inhibition and stress induction, suggesting that thiolutin use in vivo should be restricted to studies on its modes of action and not as an experimental tool.

 

This work models the costs and benefits of per- sonal information sharing, or self-disclosure, in online social networks as a networked disclosure game. In a networked population where edges rep- resent visibility amongst users, we assume a leader can influence network structure through content promotion, and we seek to optimize social wel- fare through network design. Our approach con- siders user interaction non-homogeneously, where pairwise engagement amongst users can involve or not involve sharing personal information. We prove that this problem is NP-hard. As a solution, we develop a Mixed-integer Linear Programming algorithm, which can achieve an exact solution, and also develop a time-efficient heuristic algo- rithm that can be used at scale. We conduct nu- merical experiments to demonstrate the properties of the algorithms and map theoretical results to a dataset of posts and comments in 2020 and 2021 in a COVID-related Subreddit community where privacy risks and sharing tradeoffs were particularly pronounced. 

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One of the most popular location privacy-preserving mechanisms applied in location-based services (LBS) is location obfuscation, where mobile users are allowed to report obfuscated locations instead of their real locations to services. Many existing obfuscation approaches consider mobile users that can move freely over a region. However, this is inadequate for protecting the location privacy of vehicles, as their mobility is restricted by external factors, such as road networks and traffic flows. This auxiliary information about external factors helps an attacker to shrink the search range of vehicles' locations, increasing the risk of location exposure. In this paper, we propose a vehicle traffic flow aware attack that leverages public traffic flow information to recover a vehicle's real location from obfuscated location. As a countermeasure, we then develop an adaptive strategy to obfuscate a vehicle's location by a "fake" trajectory that follows a realistic traffic flow. The fake trajectory is designed to not only hide the vehicle's real location but also guarantee the quality of service (QoS) of LBS. Our experimental results demonstrate that 1) the new threat model can accurately track vehicles' real locations, which have been obfuscated by two state-of-the-art algorithms, and 2) the proposed obfuscation method can effectively protect vehicles' location privacy under the new threat model without compromising QoS.