In this article, we seek to elucidate challenges and opportunities for differential privacy within the federal government setting, as seen by a team of differential privacy researchers, privacy lawyers, and data scientists working closely with the U.S. government. After introducing differential privacy, we highlight three significant challenges which currently restrict the use of differential privacy in the U.S. government. We then provide two examples where differential privacy can enhance the capabilities of government agencies. The first example highlights how the quantitative nature of differential privacy allows policy security officers to release multiple versions of analyses with different levels of privacy. The second example, which we believe is a novel realization, indicates that differential privacy can be used to improve staffing efficiency in classified applications. We hope that this article can serve as a nontechnical resource which can help frame future action from the differential privacy community, privacy regulators, security officers, and lawmakers.

Cyber-physical production systems consist of highly specialized software and hardware components. Most components and communication protocols are not built according to the Secure by Design principle. Therefore, their resilience to cyberattacks is limited. This limitation can be overcome with common operational pictures generated by security monitoring solutions. These pictures provide information about communication relationships of both attacked and non-attacked devices, and serve as a decision-making basis for security officers in the event of cyberattacks. The objective of these decisions is to isolate a limited number of devices rather than shutting down the entire production system. In this work, we propose and evaluate a concept for finding the devices to isolate. Our approach is based on solving the Critical Node Cut Problem with Vulnerable Vertices (CNP-V) - an NP-hard computational problem originally motivated by isolating vulnerable people in case of a pandemic. To the best of our knowledge, this is the first work on applying CNP-V in context of cybersecurity.

With the emerging trend of integrating Artificial Intelligence (AI) and robotics with manpower to enhance work systems, there will be an inevitable shift in the role of security officers in Singapore, note industry experts. While this digital transformation will free up workers' time for higher-value tasks and creates more opportunities for them to upskill and move up in the security industry, those who lack the fundamental competencies to operate new technologies may be at a disadvantage. These are some key findings from NTUC LearningHub's recently launched Industry Insights report on Security, which featured uncovered the trends in Singapore's private security sector. "To optimise headcount in the manpower scarce industry, security technology management which integrates the use of AI into its operations, allows security officers to be more competitive and productive. There are four aspects that security officers need to be familiar with: Access Control Management, Alarm System Management, Robotics and Automation Application as well as Security Surveillance Management," says NTUC LearningHub's Director of Technical Skills, Tay Ee Learn As the security industry in Singapore makes strides in leveraging technology to enhance and create more efficient security systems, there will be lower dependence on manpower to conduct manual work such as patrolling and CCTV feed monitoring.

ProPublica is a nonprofit newsroom that investigates abuses of power. Sign up for ProPublica's Big Story newsletter to receive stories like this one in your inbox as soon as they are published. Ariella Russcol specializes in drama at the Frank Sinatra School of the Arts in Queens, New York, and the senior's performance on this April afternoon didn't disappoint. While the library is normally the quietest room in the school, her ear-piercing screams sounded more like a horror movie than study hall. But they weren't enough to set off a small microphone in the ceiling that was supposed to detect aggression.

This story was co-published with ProPublica. Ariella Russcol specializes in drama at the Frank Sinatra School of the Arts in Queens, New York, and the senior's performance on this April afternoon didn't disappoint. While the library is normally the quietest room in the school, her ear-piercing screams sounded more like a horror movie than study hall. But they weren't enough to set off a small microphone in the ceiling that was supposed to detect aggression. A few days later, at the Staples Pathways Academy in Westport, Connecticut, junior Sami D'Anna inadvertently triggered the same device with a less spooky sound--a coughing fit from a lingering chest cold.

Artificial intelligence research group OpenAI last month made the unusual announcement: It had built an AI-powered content creation engine so sophisticated that it wouldn't release the full model to developers. Anyone who works in cybersecurity immediately knew why. Phishing emails, which try to trick recipients into clicking malicious links, originated 91 percent of all cyberattacks in 2016, according to a study by Cofense Inc. Combining software bots to scrape personal information from social networks and public databases with such a powerful content generation engine could produce much more persuasive phishing emails that might even mimic a certain person's writing style, said Nicolas Kseib, lead data scientist at TruSTAR Technology LLC. The potential result: Cybercriminals could launch phishing attacks much faster and on an unprecedented scale. That danger neatly sums up the never-ending war that is the state of cybersecurity today, one in which no one can yet answer a central question: Will artificial intelligence provide more help to criminals or to the people trying to stop them?

As artificial intelligence (AI) continues to move from the summer of hype to the fall tech conference news cycle, mass confusion has begun on what AI can be used for. From fears of SKYNET, to hopes for the computer in StarTrek and Jarvis in Iron Man, the value will come from defining the proper outcomes. AI is more than just a fad. With a market size of $100B by 2025, Constellation sees the AI subsets of machine learning, deep learning, natural language processing, and cognitive computing taking the market by storm (see Figure 1). The disruptive nature of AI comes from the speed, precision, and capacity of augmenting humanity.

As artificial intelligence (AI) continues to move from the summer of hype to the fall tech conference news cycle, mass confusion has begun on what AI can be used for. From fears of SKYNET, to hopes for the computer in StarTrek and Jarvis in Iron Man, the value will come from defining the proper outcomes. AI is more than just a fad. With a market size of $100B by 2025, Constellation sees the AI subsets of machine learning, deep learning, natural language processing, and cognitive computing taking the market by storm (see Figure 1). The disruptive nature of AI comes from the speed, precision, and capacity of augmenting humanity.

As artificial intelligence (AI) continues to move from the summer of hype to the fall tech conference news cycle, mass confusion has begun on what AI can be used for. From fears of SKYNET, to hopes for the computer in StarTrek and Jarvis in Iron Man, the value will come from defining the proper outcomes. AI is more than just a fad. With a market size of 100B by 2025, Constellation sees the AI subsets of machine learning, deep learning, natural language processing, and cognitive computing taking the market by storm (see Figure 1). The disruptive nature of AI comes from the speed, precision, and capacity of augmenting humanity.

While market leaders and fast followers have not yet achieved mass personalization, the next rush is focused on investments in artificial intelligence (see Figure 1). Searching for a competitive advantage and fearful of disruption, board rooms and CXO's have rushed to artificial intelligence as the next big thing. The investment in pilots for AI's subsets of machine learning, deep learning, natural language processing, and cognitive computing have moved from science projects to new digital business models powered by smart services. With the goal of precision decisions, successful AI projects require more than just great algorithms or access to data scientists. The seven success factors for AI foreshadow a world where limited players can deliver AI smart services.