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Protecting Mobile Users from Security Threats through LA Secure App

As people are increasingly relying on their devices for working, shopping, health care and connecting with friends using smartphones, they are susceptible to mobile attacks. Besides presenting vast and rich targets to hackers, mobile devices have fewer protections available.

Generally, cyberattackers increasingly target mobile devices as they often do not have the same protections as desktops and laptop computers. Recent research stated that 40% of all mobile devices are prone to cyberattacks, and found a 15% increase in banking Trojan activity, where users’ mobile banking credentials are at risk of being stolen.

Therefore, the Los Angeles Metropolitan Transportation Authority, in partnership with mobile security company has rolled out LA Secure, an application to help residents protect their devices and information from cybersecurity threats on public Wi-Fi networks, including those available on the city’s public transit system.

The app alerts users to mobile security threats from websites or malicious links in real-time so users can stop security threats before their device or information is compromised. For example, LA Secure’s phishing protection allows users to screen a link sent to them, ensuring the legitimacy of the destination website. Also, if users try to connect to a rogue Wi-Fi network, the app alerts them of the security risk right away.

Providing a safe and secure environment for Los Angeles County’s 10 million residents to live and work is becoming just as important online as it has always been offline. As L.A. County continues to expand public Wi-Fi access, including on its entire fleet of Metro buses, LA Secure offers an industry-leading technology to ensure all the activity they conduct on their mobile devices remains safe, private and secure.”

– Hilda L. Solis, Metro Board Chair and L.A. County Supervisor

The biggest risk of public Wi-Fi is the inherent insecurity of such networks. While introducing an app could help, the question is how exactly this app does that and what independent security assessments were performed to ensure that the apps are safe. However, The City of Los Angeles’s effort about protecting its citizens is a positive development. Other counties would do well to consider following suit.

The application, which is part of the city’s effort to improve customer experience, includes an enhancement that continuously validates every web connection to the device. This will allow residents to use public Wi-Fi to shop online, enter their financial details and log into secure portals without worrying about their information being stolen. Critically, the app does not collect any personal information from citizens’ mobile devices.

The mobile screening app started with basic DNS-filtering that would prevent malicious websites, but Mittal said the company’s approach is now informed by insights gathered from Zimperium’s enterprise customers who are checking for network-related attacks or application-based attacks, phishing attacks. It is the same detection technology we use for the citizens.

As reported by OpenGov Asia, U.S. researchers have discovered an inexpensive way for tech companies to implement a rigorous form of personal data privacy when using or sharing large databases for machine learning.

The researchers aim to solve the problem with a new method using a technique called locality sensitive hashing. They found they could create a small summary of an enormous database of sensitive records. The method is both safe to make publicly available and useful for algorithms that use kernel sums, one of the basic building blocks of machine learning, and for machine-learning programs that perform common tasks like classification, ranking and regression analysis.

The new method scales for high-dimensional data. The sketches are small and the computational and memory requirements for constructing them are also easy to distribute. Engineers today must either sacrifice their budget or the privacy of their users if they wish to use kernel sums. This new method changes the economics of releasing high-dimensional information with differential privacy. This latest method is simple, fast and 100 times less expensive to run than existing methods.

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