Emerging ethical approaches have attempted to filter pretraining material, but such approaches have been ad hoc and failed to take context into account. We offer an approach to filtering grounded in law, which has directly addressed the tradeoffs in filtering material.

Recent attacks on Bitcoin's peer-to-peer (P2P) network demonstrated that its transaction-flooding protocols, which are used to ensure network consistency, may enable user deanonymization--the linkage of a user's IP address with her pseudonym in the Bitcoin network. In 2015, the Bitcoin community responded to these attacks by changing the network's flooding mechanism to a different

Emerging ethical approaches have attempted to filter pretraining material, but such approaches have been ad hoc and failed to take context into account. We offer an approach to filtering grounded in law, which has directly addressed the tradeoffs in filtering material.

Driven by the great advances in metaverse and edge computing technologies, vehicular edge metaverses are expected to disrupt the current paradigm of intelligent transportation systems. As highly computerized avatars of Vehicular Metaverse Users (VMUs), the Vehicle Twins (VTs) deployed in edge servers can provide valuable metaverse services to improve driving safety and on-board satisfaction for their VMUs throughout journeys. To maintain uninterrupted metaverse experiences, VTs must be migrated among edge servers following the movements of vehicles. This can raise concerns about privacy breaches during the dynamic communications among vehicular edge metaverses. To address these concerns and safeguard location privacy, pseudonyms as temporary identifiers can be leveraged by both VMUs and VTs to realize anonymous communications in the physical space and virtual spaces. However, existing pseudonym management methods fall short in meeting the extensive pseudonym demands in vehicular edge metaverses, thus dramatically diminishing the performance of privacy preservation. To this end, we present a cross-metaverse empowered dual pseudonym management framework. We utilize cross-chain technology to enhance management efficiency and data security for pseudonyms. Furthermore, we propose a metric to assess the privacy level and employ a Multi-Agent Deep Reinforcement Learning (MADRL) approach to obtain an optimal pseudonym generating strategy. Numerical results demonstrate that our proposed schemes are high-efficiency and cost-effective, showcasing their promising applications in vehicular edge metaverses.

Advancements in AI might soon render phrases such as "hidden in the crowd" or "stay hidden in plain sight" a curious relic of the past, according to new research published last week on Nature Communications. In a paper titled "Interaction data are identifiable even across long periods of time," researchers used geometric deep learning and triplet loss optimization to successfully identify a majority of individuals from an anonymized mobile phone dataset of 40,000 people. The research is notable because fine-grained records of people's interactions, both offline and online, are collected at scale today. Tech giants such as Facebook and Google, telecommunication operators, and other businesses are known to collect and either resell data wholesale or leverage it to power data-centric services. The technique relies on how people tend to stick to established social circles and that such regular interactions form a stable pattern over time.

How you interact with a crowd may help you stick out from it, at least to artificial intelligence. When fed information about a target individual's mobile phone interactions, as well as their contacts' interactions, AI can correctly pick the target out of more than 40,000 anonymous mobile phone service subscribers more than half the time, researchers report January 25 in Nature Communications. The findings suggest humans socialize in ways that could be used to pick them out of datasets that are supposedly anonymized. It's no surprise that people tend to remain within established social circles and that these regular interactions form a stable pattern over time, says Jaideep Srivastava, a computer scientist from the University of Minnesota in Minneapolis who was not involved in the study. "But the fact that you can use that pattern to identify the individual, that part is surprising."

When technology enters applications and processes with a long tradition of controversial societal debate, multi-faceted new ethical and legal questions arise. This paper focusses on the process of whistleblowing, an activity with large impacts on democracy and business. Computer science can, for the first time in history, provide for truly anonymous communication. We investigate this in relation to the values and rights of accountability, fairness and data protection, focusing on opportunities and limitations of the anonymity that can be provided computationally; possible consequences of outsourcing whistleblowing support; and challenges for the interpretation and use of some relevant laws. We conclude that to address these questions, whistleblowing and anonymous whistleblowing must rest on three pillars, forming a 'triangle of whistleblowing protection and incentivisation' that combines anonymity in a formal and technical sense; whistleblower protection through laws; and organisational and political error culture.

On June 22nd, visitors to Slate Star Codex, a long-standing blog of considerable influence, discovered that the site's cerulean banner and graying WordPress design scheme had been superseded by a barren white layout. In the place of its usual catalogue of several million words of fiction, book reviews, essays, and miscellanea, as well as at least as voluminous an archive of reader commentary, was a single post of atypical brevity. "So," it began, "I kind of deleted the blog. The farewell post was attributed, like virtually all of the blog's entries since its inception, in 2013, to Scott Alexander, the pseudonym of a Bay Area psychiatrist--the title "Slate Star Codex" is an imperfect anagram of the alias--and it put forth a rationale for this online self-immolation. "Last week I talked to a New York Times technology reporter who was planning to write a story on Slate Star Codex," the post continued. "He told me it would be a mostly positive piece about how we were an interesting gathering place for people in tech, and how we were ahead of the curve on some aspects of the coronavirus situation." In early March, Alexander had suggested that his readers begin to prepare for potential catastrophe, and his extensive review of the available medical literature led him to the conclusion that, despite the early guidance by the Centers for Disease Control and Prevention to the contrary, masks were likely to prove more useful than not. A month later, he looked back at his forecast and awarded himself a "solid B-"--not perfect, but at least more accurate than the news media, which, with some notable exceptions, he wrote, "not only failed to adequately warn its readers about the epidemic, but actively mocked and condescended to anyone who did sound a warning." Journalists, in his view, were guilty of an inability or a refusal to weight the possible outcomes. As he put it, if there was even a ten per cent risk of a ruinous pandemic, shouldn't that have been the headline? Alexander, who prefaces some of his own posts with an "epistemic status," by which he rates his own confidence in the opinions to follow, thought the media, too, should present its findings in shades of gray. The final post went on, "It probably would have been a very nice article.

You've probably heard that automation is becoming commonplace in more fields of human endeavor. Or, in headline-speak: "Are Robots Coming for Your Job?" You may also have heard that the last bastions of human exclusivity will probably be creativity and artistic judgment. Robots will be washing our windows long before they start creating masterpieces. In reporting a story for CBS Sunday Morning, for example, I recently visited Rutgers University's Art and Artificial Intelligence Laboratory, where Ahmed Elgammal's team has created artificial-intelligence software that generates beautiful, original paintings.