In typical collective decision making scenarios, rank aggregation aims to combine different agents' preferences over the given alternatives into an aggregated ranking that agrees the most with all the preferences. However, since the aggregation procedure relies on a data curator, the privacy within the agents' preference data could be compromised when the curator is untrusted. All existing works that guarantee differential privacy in rank aggregation assume that the data curator is trusted. In this paper, we first formulate and address the problem of locally differentially private rank aggregation, in which the agents have no trust in the data curator. We propose an effective and efficient protocol LDP-KwikSort, with the appealing property that each agent only needs to answer a small number of pairwise comparison queries from the untrusted curator with controllable noise, and the aggregated ranking could maintain an acceptable utility compared with that of the non-private protocol. Theoretical and empirical results demonstrate that the proposed solution can achieve the practical trade-off between the utility of aggregated ranking and the privacy preserving of agents' pairwise preferences.
Summary Jaccard Similarity has been widely used to measure the distance between two sets (or preference profiles) owned by two different users. Yet, in the private data collection scenario, it requires the untrusted curator could only estimate an approximately accurate Jaccard similarity of the involved users but without being allowed to access their preference profiles. This paper aims to address the above requirements by considering the local differential privacy model. To achieve this, we initially focused on a particular hash technique, MinHash, which was originally invented to estimate the Jaccard similarity efficiently. We designed the PrivMin algorithm to achieve the perturbation of MinHash signature by adopting Exponential mechanism and build the Locally Differentially Private Jaccard Similarity Estimation (LDP‐JSE) protocol for allowing the untrusted curator to approximately estimate Jaccard similarity. Theoretical and empirical results demonstrate that the proposed protocol can retain a highly acceptable utility of the estimated similarity as well as preserving privacy.
With the advent of Industry 4.0, cloud computing techniques have been increasingly adopted by industry practitioners to achieve better workflows. One important application is cloud-based decision-making, in which multiple enterprise partners need to arrive an agreed decision. Such cooperative decision-making problem is sometimes formed as a weighted voting game, in which enterprise partners express 'YES/NO' opinions. Nevertheless, existing cryptographic approaches to Cloud-Based Weighted Voting Game have restricted collusion tolerance and heavily rely on trusted servers, which are not always available. In this work, we consider the more realistic scenarios of having semi-honest cloud server/partners and assuming maximal collusion tolerance. To resolve the privacy issues in such scenarios, the DPWeVote protocol is proposed which incorporates Randomized Response technique and consists the following three phases: the Randomized Weights Collection phase, the Randomized Opinions Collection phase, and the Voting Results Release phase. Experiments on synthetic data have demonstrated that the proposed DPWeVote protocol managed to retain an acceptable utility for decision-making while preserving privacy in semihonest environment.
This study investigated the distribution, pollution level and potential ecological risk of potentially toxic elements (PTEs) from manganese mining in a karstic Danshui River, in Changyang, Western Hubei, Central China. River water and sediments were collected for seven PTEs measurement (As, Cd, Cr, Cu, Mn, Pb and Zn), as well as pH and Eh of the river water were measured. Results showed that the major pollutant was Mn, the river water environment was mainly acidic and oxidizing (288 < Eh, pH < 6.3), and the pollution distribution of Mn in the study area was dominated by the combination of natural processes and anthropogenic activities. In the river water, according to the contamination factor (CF) and pollution load index (IPL) results, Mn was considered the main pollutant. There was low As and Pb pollution downstream as well as Cu pollution upstream. Upstream and downstream areas were the main polluted river sections of the river water samples collected. In river sediments, based on the results of the geo-accumulation index (Igeo) and potential ecological risk index (IPER), it was determined that there was only considerable Mn pollution. The IPER of the PTEs from the river sediments was at acceptable levels, only Mn upstream performed at a moderate ecological risk level. According to Pearson correlation and principal component analysis, Mn originated from manganese mining activities, Cd, Cr and Zn were of natural origin, and Cu may have come from both mining and natural origin, whereas Pb and As were mainly related to the daily activities. Consequently, elemental speciation, mining activities and the distribution of water conservancy facilities were the main impacts of PET pollution distribution in this river.
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