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Fox News chief political anchor Bret Baier has the latest on the pros and cons of the bombshell developments on "Special Report." ChatGPT and other large language models (LLMs) have become amazing helpers for everyday tasks. Whether it's summarizing complex ideas, designing a birthday card or even planning your apartment's layout, you can get impressive results with just a simple prompt. But as helpful as these AI tools are, their convenience comes with hidden risks, especially when it comes to your personal privacy. Join the FREE "CyberGuy Report": Get my expert tech tips, critical security alerts and exclusive deals, plus instant access to my free "Ultimate Scam Survival Guide" when you sign up!

Some of the world's most popular apps are likely being co-opted by rogue members of the advertising industry to harvest sensitive location data on a massive scale, with that data ending up with a location data company whose subsidiary has previously sold global location data to US law enforcement. The thousands of apps, included in hacked files from location data company Gravy Analytics, include everything from games like Candy Crush and dating apps like Tinder to pregnancy tracking and religious prayer apps across both Android and iOS. Because much of the collection is occurring through the advertising ecosystem--not code developed by the app creators themselves--this data collection is likely happening without users' or even app developers' knowledge. This article was created in partnership with 404 Media, a journalist-owned publication covering how technology impacts humans. "For the first time publicly, we seem to have proof that one of the largest data brokers selling to both commercial and government clients appears to be acquiring their data from the online advertising'bid stream,'" rather than code embedded into the apps themselves, Zach Edwards, senior threat analyst at cybersecurity firm Silent Push and who has followed the location data industry closely, tells 404 Media after reviewing some of the data.

The development of tools and techniques to analyze and extract organizations data habits from privacy policies are critical for scalable regulatory compliance audits. Unfortunately, these tools are becoming increasingly limited in their ability to identify compliance issues and fixes. After all, most were developed using regulation-agnostic datasets of annotated privacy policies obtained from a time before the introduction of landmark privacy regulations such as EUs GDPR and Californias CCPA. In this paper, we describe the first open regulation-aware dataset of expert-annotated privacy policies, C3PA (CCPA Privacy Policy Provision Annotations), aimed to address this challenge. C3PA contains over 48K expert-labeled privacy policy text segments associated with responses to CCPA-specific disclosure mandates from 411 unique organizations. We demonstrate that the C3PA dataset is uniquely suited for aiding automated audits of compliance with CCPA-related disclosure mandates.

Despite years worth of efforts to eliminate the scourge of ransomware targeting schools, hospitals, and critical infrastructure worldwide, experts are warning that the crisis is only heating up, with criminal gangs growing ever more aggressive in their tactics. The threat of real-world violence now looms, some experts warn, as the data stolen grows increasingly sensitive and millions in potential profits hang in the balance. "We know where your CEO lives," read a message reportedly received by one victim. Attacks targeting the medical sector are blooming in response to the 44 million payout by Change Healthcare this March. United States lawmakers and intelligence officials are circling their wagons following the revelation of Israel's involvement in a malign influence campaign that targeted US voters--an attempt by America's Middle East ally to artificially boost support for an increasingly unpopular war that was kicked off by Hamas' unprecedented Oct. 7th attack.

Shortly after I became pregnant with my second child, in the fall of 2022, I decided to try a modest experiment. I wanted to see whether I could hide my pregnancy from my phone. After spending my twenties eagerly surveilling and sharing the details of my life online, I had already begun trying to erect some walls of technological privacy: I'd deleted most apps on my phone and turned off camera, location, and microphone access for nearly all of the ones that I did have; I had disabled Siri--I just found it annoying--and I didn't have any smart devices. For the experiment, I would abide by some additional restrictions. I wouldn't Google anything about pregnancy nor shop for baby stuff either online or using a credit card, and neither would my husband, because our I.P. addresses--and thus the vast, matrixed fatbergs of personal data assembled by unseen corporations to pinpoint our consumer and political identities--were linked.

For as little as $0.12 per record, data brokers in the US are selling sensitive private data about both active-duty military members and veterans, including their names, addresses, geolocation, net worth, and religion, and information about their children and health conditions. In an unsettling study published today, researchers from Duke University approached 12 data brokers and purchased thousands of records about American service members with minimal vetting. The study highlights the extreme privacy and national security risks created by data brokers. These companies are part of a shadowy multibillion-dollar industry that collects, aggregates, buys, and sells data, practices that are currently legal in the US, exacerbating the erosion of personal and consumer privacy. Last week, President Biden released his executive order on AI, a sweeping set of rules and guidelines designed to improve AI safety and security.

OpenAI's ChatGPT technology has become the talk of the town, with its capabilities seemingly drawn from the realms of science fiction. Impressive artworks and complex texts being produced without humans – so far so cool. But have you realised the potential privacy issues which the revolutionary technology potentially creates? This technology, along with rivals such as Google's Bard, is attracting millions of queries and searches every day. ChatGPT alone had gained over 100m users by January 2023, making it the fastest-growing consumer application ever.[1]

Here's an experiment being run by undergraduate computer science students everywhere: Ask ChatGPT to generate phishing emails, and test whether these are better at persuading victims to respond or click on the link than the usual spam. It's an interesting experiment, and the results are likely to vary wildly based on the details of the experiment. Bruce Schneier is a lecturer and fellow at the Harvard Kennedy School and chief of security architecture at Inrupt. His latest book is A Hacker's Mind. Barath Raghavan is a professor of computer science at USC and cofounder of INVISV.

A data marketplace is an online venue that brings data owners, data brokers, and data consumers together and facilitates commoditisation of data amongst them. Data pricing, as a key function of a data marketplace, demands quantifying the monetary value of data. A considerable number of studies on data pricing can be found in literature. This paper attempts to comprehensively review the state-of-the-art on existing data pricing studies to provide a general understanding of this emerging research area. Our key contribution lies in a new taxonomy of data pricing studies that unifies different attributes determining data prices. The basis of our framework categorises these studies by the kind of market structure, be it sell-side, buy-side, or two-sided. Then in a sell-side market, the studies are further divided by query type, which defines the way a data consumer accesses data, while in a buy-side market, the studies are divided according to privacy notion, which defines the way to quantify privacy of data owners. In a two-sided market, both privacy notion and query type are used as criteria. We systematically examine the studies falling into each category in our taxonomy. Lastly, we discuss gaps within the existing research and define future research directions.