Metal-organic networks (MONs) is a family of chemical compounds consisting of clusters or metal ions and organic ligands. These are studied as one, two or three dimensional structures of porous materials and subclasses of coordination polymers. MONs are mostly used in catalysis for the separation & purification of gases and as conducting solids or super-capacitors. In some situations, these networks are found to be stable in the process of removal or solvent of the guest molecules and could be restored with some other chemical compounds. The physical stability and mechanical properties of these networks have become a topic of great interest due to the aforesaid characteristics. Topological indices (TIs) are numeric quantities that are used to forecast the natural relationships among the physico-chemical characteristics of the chemical compounds in their fundamental network. During the studies of the MONs, TIs show an essential role in the theoretical & environmental chemistry and pharmacology. In this paper, we compute various latest developed degree-based TIs for two different metal-organic networks with increasing number of layers consisting on both metal and organic ligands vertices as well. A comparison among the computed different versions of the TIs with the help of the numerical values and their graphs is also included. INDEX TERMS Topological indices, chemical compounds, metals-organic networks.
Proxy Re-Encryption (PRE) is a cryptographic primitive that allows a proxy to turn an Alice’s ciphertext into a Bob’s ciphertext on the same plaintext. All of the PRE schemes are public key encryption and semantic security. Deterministic Public Key Encryption (D-PKE) provides an alternative to randomized public key encryption in various scenarios where the latter exhibits inherent drawbacks. In this paper, we construct the first multi-use unidirectional D-PRE scheme from Lattices in the auxiliary-input setting. We also prove that it is PRIV1-INDr secure in the standard model based on the LWR. Finally, an identity-based D-PRE is obtained from the basic construction.
Genome-wide association studies can provide researchers some reference on gene mapping of complex trait, a key point of which is how to improve the power of association test. Recently, two-stage approaches are widely used to genome-wide association analysis. In the first stage, a screening test is used to select markers, and in the second stage, a family-based association test is performed based on a smaller set of the selected markers. Here, we modify an existing two-stage approach and propose a new test statistic for the association analysis. Simulation studies are conducted to compare the type I error rates and powers of the proposed approach with those of the existing two-stage approaches. Simulation results show that the new two-stage approach has greater power than the other two-stage approaches to some extent.
Analyzing trajectory data can provide people with a higher quality of life. However, publishing trajectory data directly will leak privacy. The authors propose a trajectory data publication method based on differential privacy (TDDP). TDDP method consists of two stages. In the location generalization stage, firstly, the locations at each timestamp are clustered into classes by k-means++ algorithm, and then the representative location of each class is selected by using the exponential mechanism. In the generalized trajectory data publication stage, the authors design a sampling mechanism to form the generalized trajectories. The locations are sampled from the representative locations under different timestamps to form the generalized trajectories. The TDDP method can avoid the generation of non-semantic representative locations and ensure that the generalized trajectories can resist filtering attacks. The experimental results show that the trajectory data released by TDDP method can achieve a good balance between privacy protection and data availability.
In this paper, we study the privacy-preserving data publishing problem in a distributed environment. The data contain sensitive information; hence, directly pooling and publishing the local data will lead to privacy leaks. To solve this problem, we propose a multiparty horizontally partitioned data publishing method under differential privacy (HPDP-DP). First, in order to make the noise level of the published data in the distributed scenario the same as in the centralized scenario, we use the infinite divisibility of the Laplace distribution to design a distributed noise addition scheme to perturb the locally shared data and use Paillier encryption to transmit the locally shared data to the semitrusted curator. Then, the semitrusted curator obtains the estimator of the covariance matrix of the aggregated data with Laplace noise and then obtains the principal components of the aggregated data and returns them to each data owner. Finally, the data owner utilizes the generative model of probabilistic principal component analysis to generate a synthetic data set for publication. We conducted experiments on different real data sets; the experimental results demonstrate that the synthetic data set released by the HPDP-DP method can maintain high utility.
Data are distributed between different parties. Collecting data from multiple parties for analysis and mining will serve people better. However, it also brings unprecedented privacy threats to the participants. Therefore, safe and reliable data publishing among multiple data owners is an urgent problem to be solved. We mainly study the problem of privacy protection in data publishing. For a centralized scenario, we propose the LDA-DP algorithm. First, the within-class mean vectors and the pooled within-class scatter matrix are perturbed by the Gaussian noise. Second, the optimal projection direction vector with differential privacy is obtained by the Fisher criterion. Finally, the low-dimensional projection data of the original data are obtained. For distributed scenarios, we propose the Mul-LDA-DP algorithm based on a blockchain and differential privacy technology. First, the within-class mean vectors and within-class scatter matrices of local data are perturbed by the Gaussian noise and uploaded to the blockchain network. Second, the projection direction vector is calculated in the blockchain network and returned to the data owner. Finally, the data owner uses the projection direction vector to generate low-dimensional projection data of the original data and upload it to the blockchain network for publishing. Furthermore, in a distributed scenario, we propose a correlated noise generation scheme that uses the additivity of the Gaussian distribution to mitigate the effects of noise and can achieve the same noise level as the centralized scenario. We measure the utility of the published data by the SVM misclassification rate. We conduct comparative experiments with similar algorithms on different real data sets. The experimental results show that the data released by the two algorithms can maintain good utility in SVM classification.
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