A huge data breach has leaked over 50,000 profiles from the'Gay Daddy' dating app, cybersecurity researchers have discovered. The exposed data contains extremely sensitive information including users' names, ages, location data and HIV status. According to experts from Cybernews, the exposed database also contains over 124,000 private messages and photos – many of which are explicit. While the app markets itself as a'private and anonymous community', researchers say the information could be accessed by anyone with'basic technical knowledge'. Researchers say the app's'devastating' security failure puts its users at serious risk of blackmail, exploitation and even physical harm.

The US government says a Chinese gaming company's ownership of the gay dating app Grindr poses a national security risk, according to a report from Reuters. Beijing Kunlun Tech acquired a 60 percent stake in Grindr in 2016 and bought the rest in 2018. But, Reuters reports, the Chinese firm didn't clear the acquisition with the agency known as the Committee on Foreign Investment in the United States, or CFIUS, which evaluates the national security impacts of foreign investments in US companies. Kunlun is now seeking to sell Grindr following the CFIUS assessment, according to Reuters. Grindr declined to comment; CFIUS and Kunlun did not respond to requests for comment.

An error on the dating app Grindr allowed third party sites to access personal information. Tony Spitz has the details. Dating app Grindr, which serves many LGBTQ users, admits it has been sharing users' HIV status with third-party companies. Grindr says it will stop sharing user data, including HIV status, to two other companies, after concerns the disclosures violated consumer privacy and undermined public health efforts. The gay dating and social networking app, which counts over 3 million daily active users, said Tuesday it would no longer share users' HIV status with app optimization company Apptimize and is discussing how to remove data from Localytics.

Gay dating app Grindr has said it will stop sharing its users' HIV status with other companies after it was heavily criticised for distributing the information to third parties. Tech firms Apptimize and Localytics, which help to manage the app's performance, had been provided with the data. As the HIV information is transferred alongside GPS, phone ID data, and email, users could be identified along with their HIV status, according to Antoine Pultier, a researcher at Norwegian non profit organisation, SINTEF, which first raised the issue. In response, Grindr's chief technology officer, Scott Chen, said sharing data with partners to test and optimise its platform was "industry practice". He insisted sensitive data was encrypted when sent and vendors were bound by strict contractual terms to ensure it is kept secure and confidential.

Grindr was confronted with questions about security flaws as recently as last week after NBC reported private information about users, including unread messages, deleted photos and location data, were being collected by a property management startup through a website that Grindr built. Grindr says it has since fixed the flaw and shut down the website, which allowed users to see who blocked them on the app.

This paper proposes a new neuro-rough model for modelling the risk of HIV from demographic data. The model is formulated using Bayesian framework and trained using Markov Chain Monte Carlo method and Metropolis criterion. When the model was tested to estimate the risk of HIV infection given the demographic data it was found to give the accuracy of 62% as opposed to 58% obtained from a Bayesian formulated rough set model trained using Markov chain Monte Carlo method and 62% obtained from a Bayesian formulated multi-layered perceptron (MLP) model trained using hybrid Monte. The proposed model is able to combine the accuracy of the Bayesian MLP model and the transparency of Bayesian rough set model. Keywords: Neuro-rough model, multi-layered perceptron, Bayesian, HIV modelling Introduction The role of machine learning is to be able to make predictions given a set of inputs.