Transport for London (TfL) is planning to roll out a system to track commuters making use of public Wi-Fi hotspots across the London Underground in coming months.
The UK transport agency said on Wednesday that “secure, privacy-protected data collection will begin on 8 July 2019,” with improved customer services — including warnings over delays and station congestion — expected to appear later in the year.
The rollout builds upon a trial of the technology in 2016. At the time, TfL said the four-week test was launched to “give TfL a more accurate understanding of how people move through stations, interchange between services and how crowding develops.”
Now, TfL says that tracking the data generated by commuters will also give the agency an insight in where to “prioritize transport investment.”
See also: Transport for London’s contactless and mobile device payments system will be adopted in some American cities
Wi-Fi hotspots were introduced during the pilot across 54 stations within Zones 1 to 4. If a passenger had Wi-Fi enabled, the hotspots would record connection attempts and connectivity searches through their MAC addresses.
In a period of four weeks, over 509 million pieces of data were collected from 5.6 million mobile devices across 42 million journeys.
Now, the information pool is likely to expand exponentially. However, it is only connection requests which are collected, and no browsing activities or histories are accessed.
TfL’s aim is to generate information on the network through more than 260 Wi-Fi access points in the Underground without needing to rely on travel records gleaned from tickets alone.
TechRepublic: How has GDPR actually affected businesses?
“Currently, TfL uses data from its ticketing system to understand how journeys are made across the network,” the agency explained. “While this is accurate for people entering and exiting the stations, this data cannot show the flow of movement through a station. Using depersonalized Wi-Fi data will give a more accurate, almost real-time, understanding of the flow of people through stations or interchanging between services.”
In addition, TfL hopes that the data may eventually be used to give commuters up-to-date information on crowding through the transport authority’s website and apps — as well as boost ad revenue by tracking footfall more accurately.
An API with access to commuter information is also in the works for the development of “new products and services.”
While the agency says every scrap of data is depersonalized, the data collection will be enabled by default. Commuters that wish to opt-out will need to turn their Wi-Fi setting off or enable Flight Mode when they enter the Underground.
CNET: Instagram influencer data taken offline after exposure
Privacy concerns generally surface when data-slurping practices are launched with an opt-in set as default. In order to try and allay such fears, TfL says it has “worked closely” with the UK’s Information Commissioner’s Office (ICO) in the handling of data management and transparency.
Customers will soon see posters and notices warning them of the upcoming infrastructure changes, and while the technology is rolled out and tested across the Wi-Fi network, data will not be stored.
Previous and related coverage
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Managing Vulnerabilities in a Cloud Native World
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Security Tools Help Bring Dev and Security Teams Together
Software development teams are increasingly focused on identifying and mitigating any issues as quickly and completely as possible. This relates not only to software quality but also software security. Different organizations are at different levels when it comes to having their development teams and security teams working in concert, but the simple fact remains that there are far more developers out there than security engineers.
Those factors are leading organizations to consider security tooling and automation to proactively discover and resolve any software security issues throughout the development process. In the recent report, “GigaOm Radar for Developer Security Tools,” Shea Stewart examines a roundup of security tools aimed at software development teams.
Stewart identified three critical criteria to bear in mind when evaluating developer security tools. These include:
- Vendors providing tools to improve application security can and should also enhance an organization’s overall security posture.
- The prevailing “shift-left” mindset doesn’t necessarily mean the responsibility for reducing risk should shift to development, but instead focusing on security earlier in the process and continuing to do so throughout the development process will reduce risk and the need for extensive rework.
- Security throughout the entire software development lifecycle (SDLC) is critical for any organization focused on reducing risk.
Figure 1. How Cybersecurity Applies Across Each Stage of the Software Development Lifecycle *Note: This report focuses only on the Developer Security Tooling area
Individual vendors have made varying levels of progress and innovation toward enhancing developer security. Following several acquisitions, Red Hat, Palo Alto Networks, and Rapid7 have all added tooling for developer security to their platforms. Stewart sees a couple of the smaller vendors like JFrog and Sonatype as continuing to innovate to remain ahead of the market.
Vendors delving into this category and moving deeper into “DevSecOps” all seem to be taking different approaches to their enhanced security tooling. While they are involving security in every aspect of the development process, some tend to be moving more quickly to match the pace of the SDLC. Others are trying to shore up existing platforms by adding functionality through acquisition. Both infrastructure and software developers are now sharing toolsets and processes, so these development security tools must account for the requirements of both groups.
While none of the 12 vendors evaluated in this report can provide comprehensive security throughout the entire SDLC, they all have their particular strengths and areas of focus. It is therefore incumbent upon the organization to fully and accurately assess its SDLC, involve the development and security teams, and match the unique requirements with the functionality provided by these tools. Even if it involves using more than one at different points throughout the process, focus on striking a balance between stringent security and simplifying the development process.
Read more: Key Criteria for Evaluating Developer Security Tools, and the Gigaom Radar for Developer Security Tool Companies.
The post Security Tools Help Bring Dev and Security Teams Together appeared first on Gigaom.
Key Criteria for Evaluating User and Entity Behavior Analytics (UEBA)
Cybersecurity is a multidisciplinary practice that not only grows in complexity annually but evolves nearly as quickly. A survey of the security landscape today would reveal concerns ranging from the classic compromised servers to the relatively new DevSecOps practices aimed at securing the rapid deployment of new code and infrastructure. However, some things remain constant no matter how much change is introduced. While technology evolves and complexity varies, there is almost always a human component in
risks presented to an organization.
User Behavior Analysis (UBA) was designed to analyze the actions of users in an organization and attempt to identify normal and abnormal behaviors. From this analysis, malicious or risky behaviors can be detected. UBA solutions identify events that are not detectable using other methods because, unlike classic security tools (an IDS or SIEM for example), UBA does not simply pattern match or apply rule sets to data to identify security events. Instead, it looks for any and all deviations from baseline user activity.
As technology advanced and evolved, and the scope of what is connected to the network grew, the need to analyze entities other than users emerged. In response, entity analysis has been added to UBA to create UEBA or User and Entity Behavior Analysis. The strategy remains the same, but the scope of analysis has expanded to include entities involving things like daemons, processes, infrastructure, and so on.
How to Read this Report
This GigaOm report is one of a series of documents that helps IT organizations assess competing solutions in the context of well-defined features and criteria. For a fuller understanding consider reviewing the following reports:
Key Criteria report: A detailed market sector analysis that assesses the impact that key product features and criteria have on top-line solution characteristics—such as scalability, performance, and TCO—that drive purchase decisions.
GigaOm Radar report: A forward-looking analysis that plots the relative value and progression of vendor solutions along multiple axes based on strategy and execution. The Radar report includes a breakdown of each vendor’s offering in the sector.
Solution Profile: An in-depth vendor analysis that builds on the framework developed in the Key Criteria and Radar reports to assess a company’s engagement within a technology sector. This analysis includes forward-looking guidance around both strategy and product.
The post Key Criteria for Evaluating User and Entity Behavior Analytics (UEBA) appeared first on Gigaom.
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