Bibliometric Survey of Privacy of Social Media Network Data Publishing

We are witness to see exponential growth of the social media network since the year 2002. Leading social media networking sites used by people are Twitter, Snapchats, Facebook, Google, and Instagram, etc. The latest global digital report (Chaffey and Ellis-Chadwick 2019) states that there exist more than 800 million current online social media users, and the number is still exploding day by day. Users share their day to day activities such as their photos and locations etc. on social media platforms. This information gets consumed by third party users, like marketing companies, researchers, and government firms. Depending upon the purpose, there is a possibility of misuse of the user\u27s personal & sensitive information. Users\u27 sensitive information breaches can further utilized for building a personal profile of individual users and also lead to the unlawful tracing of the individual user, which is a major privacy threat. Thus it is essential to first anonymize users\u27 information before sharing it with any of the third parties. Anonymization helps to prevent exposing sensitive information to the third party and avoids its misuse too. But anonymization leads to information loss, which indirectly affects the utility of data; hence, it is necessary to balance between data privacy and utility of data. This research paper presents a bibliometric analysis of social media privacy and provides the exact scope for future research. The research objective is to analyze different research parameters and get insights into privacy in Social Media Network (OSN). The research paper provides visualization of the big picture of research carried on the privacy of the social media network from the year 2010 to 2019 (covers the span of 19 years). Research data is taken from different online sources such as Google Scholar, Scopus, and Research-gate. Result analysis has been carried out using open source tools such as Gephi and GPS Visualizer. Maximum publications of privacy of the social media network are from articles and conferences affiliated to the Chinese Academy of Science, followed by the Massachusetts Institute of Technology. Social networking is a frequently used keyword by the researchers in the privacy of the online social media network. Major Contribution in this subject area is by the computer science research community, and the least research contribution is from art and science. This study will clearly give an understanding of contributions in the privacy of social media network by different organizations, types of contributions, more cited papers, Authors contributing more in this area, the number of patents in the area, and overall work done in the area of privacy of social media network

Mobility on Demand (MOD) Sandbox Demonstration: Pierce Transit Limited Access Connections, Evaluation Report

This report presents the results of an independent evaluation of the Pierce Transit Limited Access Connections Demonstration project, part of the Federal Transit Administration (FTA) Mobility on Demand (MOD) Sandbox program. In this MOD Sandbox project, Pierce County Public Transportation Benefit Area Corporation (Pierce Transit), in partnership with Sound Transit and Lyft, implemented a first/last-mile system to provide access to rail stations and other destinations of interest within the Tacoma, Washington, metropolitan region. The one-year demonstration period began May 2018 and was extended seven months to the end of 2019, delivering 10,825 trips to 330 different users. The project increased transit use among users and reduced net vehicle miles traveled (VMT); however, it did not result in increased transit ridership overall. The service reduced parking lot use and travel and wait times for users. In addition, the project provided service to a local college, Pierce College Puyallup, which improved the perception of transit service quality and enabled greater mobility security through a guaranteed ride home. It also produced a number of lessons learned related to deployment regarding approaches to marketing, contracting, and cost management and operator negotiation

A Chemical Chaperone That Prevents Insulin Fibrillation

Insulin, a peptide hormone, is susceptible to amyloid formation upon exposure to aberrant physiological conditions, result-ing in a loss of its bioactivity. For mitigating insulin aggregation, we report a molecule called PAD-S, which completely inhibit-ed insulin fibril formation, and preserved insulin in its soluble form. Circular Dichroism spectroscopy showed that PAD-S was able to maintain the native structure of insulin, thus acting as a chemical chaperone. Seeded aggregation kinetics suggest that PAD-S inhibited primary nucleation events during aggregation. This is consistent with molecular docking results which suggest that PAD-S binds strongly to native insulin monomers/dimers. Through a competitive binding experiment with ‘LVEALYL’ peptide, we conclude that PAD-S likely binds to the amyloid prone B11-B17 residues of insulin thereby prevent-ing its aggregation. PAD-S was also effective in disaggregating preformed insulin fibrils to non-toxic species. PAD-S treated insulin was functional as indicated by its ability to phosphorylate Akt. PAD-S was also highly effective in preventing the ag-gregation of insulin biosimilars. The low cellular cytotoxicity of PAD-S, and amelioration of aggregation-induced toxicity by PAD-S treated insulin further highlights its potential as an effective chemical chaperone

REVIEW ON PARAMETER OPTIMIZATION OF SELF LUBRICATING BEARING

The Self Lubricating Bearing is used in Electronic Motors, Fuel Pumps, Actuators, Household Appliances etc. Self Lubricating Bearings are manufactured only by Powder Metallurgy, since Porosity needed for Oil Content can be achieved only by Powder Metallurgy. High Oil Content is desired for better operation of bearing. Oil Content depends on parameters like Powder Weight, Density of Component, Sintering Temperature and Sintering Speed etc. The Bearing manufactured by GKN Sinter Metals has Weight around 22-25 gm andDensity around 5.8-6.0 gm/cc with Oil content about 18-20%. The main objective of the project is to optimize the parameter values to achiev

Review On Parameter Optimization Of Self Lubricating Bearing

The Self Lubricating Bearing is used in Electronic Motors, Fuel Pumps, Actuators, Household Appliances etc. Self Lubricating Bearings are manufactured only by Powder Metallurgy, since Porosity needed for Oil Content can be achieved only by Powder Metallurgy. High Oil Content is desired for better operation of bearing. Oil Content depends on parameters like Powder Weight, Density of Component, Sintering Temperature and Sintering Speed etc. The Bearing manufactured by GKN Sinter Metals has Weight around 22-25 gm andDensity around 5.8-6.0 gm/cc with Oil content about 18-20%. The main objective of the project is to optimize the parameter values to achiev

Privacy Preservation in Online Social Networks Using Multiple-Graph-Properties-Based Clustering to Ensure k-Anonymity, l-Diversity, and t-Closeness

As per recent progress, online social network (OSN) users have grown tremendously worldwide, especially in the wake of the COVID-19 pandemic. Today, OSNs have become a core part of many people’s daily lifestyles. Therefore, increasing dependency on OSNs encourages privacy requirements to protect users from malicious sources. OSNs contain sensitive information about each end user that intruders may try to leak for commercial or non-commercial purposes. Therefore, ensuring different levels of privacy is a vital requirement for OSNs. Various privacy preservation methods have been introduced recently at the user and network levels, but ensuring k-anonymity and higher privacy model requirements such as l-diversity and t-closeness in OSNs is still a research challenge. This study proposes a novel method that effectively anonymizes OSNs using multiple-graph-properties-based clustering. The clustering method introduces the goal of achieving privacy of edge, node, and user attributes in the OSN graph. This clustering approach proposes to ensure k-anonymity, l-diversity, and t-closeness in each cluster of the proposed model. We first design the data normalization algorithm to preprocess and enhance the quality of raw OSN data. Then, we divide the OSN data into different clusters using multiple graph properties to satisfy the k-anonymization. Furthermore, the clusters ensure improved k-anonymization by a novel one-pass anonymization algorithm to address l-diversity and t-closeness privacy requirements. We evaluate the performance of the proposed method with state-of-the-art methods using a “Yelp real-world dataset”. The proposed method ensures high-level privacy preservation compared to state-of-the-art methods using privacy metrics such as anonymization degree, information loss, and execution time

Amphiphilic Small Molecule Assemblies to Enhance the Solubility and Stability of Hydrophobic Drugs

We report the synthesis of gemini-type amphiphilic molecules that form stable assemblies in aqueous medium. The assembly property of molecule M2 in aqueous solution was first inferred from peak broadening observed in the proton NMR spectrum. This was supported by dynamic light scattering and transmission electron microscopy analysis. The assembly formed from M2 (M2agg) was used to solubilize the hydrophobic drugs curcumin and doxorubicin at physiological pH. M2agg was able to effectively solubilize curcumin as well as protect it from degradation under UV irradiation. Upon solubilization in M2agg, curcumin showed excellent cell permeability, and higher toxicity to cancer cells over normal cells, likely due to enhanced cellular uptake and increased stability. M2agg also showed pH-dependent release of doxorubicin, resulting in controlled toxicity on cancer cell lines, making it a promising candidate for the selective delivery of drugs to cancer cells