Lifeboat Foundation: Safeguarding Humanity
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Category: mobile phones – Page 101

“[It is] encouraging to see this long-term, collaborative approach to privacy-protective personalized advertising from Google,” Graham Mudd, vice president of product marketing, ads and business at Facebook said on Twitter. “We look forward to continued work with them and the industry on privacy-enhancing tech through industry groups.”

Google said it will continue to support the current identifiers for the next two years, which means other companies have time to implement changes.

Apple was criticized by Facebook and other companies for rolling out its App Tracking Transparency feature, which reduces targeting capabilities by limiting advertisers from accessing an iPhone user identifier. With that change, users were given a pop-up window that let them block apps from tracking their data for advertising purposes.

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Lithium-sulfur batteries have three times the potential charge capacity of lithium-ion batteries, which are found in everything from smartphones to electric cars. Their inherent instability, however, have so far made them unsuitable for commercial applications, with lithium-sulfur batteries undergoing a 78 per cent change in size every charging cycle.

Overcoming this issue would not only radically improve the performance of battery-powered devices, it would also address some of the environment concerns that come with lithium-ion batteries, such as the sourcing and disposal of rare raw materials.

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SAN FRANCISCO/SHANGHAI, Feb 8 (Reuters) — SoftBank Group Corp (9984.T) has shelved its blockbuster sale of Arm Ltd to U.S. chipmaker Nvidia Corp (NVDA.O) valued at up to $80 billion citing regulatory hurdles and will instead seek to list the company.

Britain’s Arm, which named a new CEO on Tuesday, said it would go public before March 2023 and SoftBank CEO Masayoshi Son indicated that would be in the United States, most likely the Nasdaq.

SoftBank acquired Arm, whose technology powers Apple’s iPhone and nearly all other smartphones, in 2016 for $32 billion.

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The endless parade of bad news for Israeli malware merchant NSO Group continues. While it appears someone might be willing to bail out the beleaguered company, it still has to do business as the poster boy for the furtherance of human rights violations around the world. That the Israeli government may have played a significant part in NSO’s sales to known human rights violators may ultimately be mitigating, but for now, NSO is stuck playing defense with each passing news cycle.

Late last month, the New York Times revealed some very interesting things about NSO Group. First, it revealed the company was able to undo its built-in ban on searching US phone numbers… provided it was asked to by a US government agency. The FBI took NSO’s powerful Pegasus malware for a spin in 2019, but under an assumed name: Phantom. With the permission of NSO and the Israeli government, the malware was able to target US numbers, albeit ones linked to dummy phones purchased by the FBI.

The report noted the FBI liked what it saw, but found the zero-click exploit provided by NSO’s bespoke “Phantom” (Pegasus, but able to target US numbers) might pose constitutional problems the agency couldn’t surmount. So, it walked away from NSO. But not before running some attack attempts through US servers — something that was inadvertently exposed by Facebook and WhatsApp in their lawsuit against NSO over the targeting of WhatsApp users. An exhibit declared NSO was using US servers to deliver malware, something that suggested NSO didn’t care about its self-imposed restrictions on US targeting. In reality, it was the FBI and NSO running some tests on local applications of zero-click malware that happened to be caught by Facebook techies.

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Researchers at the U.S. Department of Energy’s (DOE) Princeton Plasma Physics Laboratory (PPPL) have gained insight into a fundamental process found throughout the universe. They discovered that the magnetic fields threading through plasma, the charged state of matter composed of free electrons and atomic nuclei, can affect the coming together and violent snapping apart of the plasma’s magnetic field lines. This insight could help scientists predict the occurrence of coronal mass ejections, enormous burps of plasma from the sun that could threaten satellites and electrical grids on Earth.

The scientists focused on the role of guide fields, magnetic fields threading through blobs, or chunks, known as plasmoids. The guide fields add rigidity to the system and ultimately affect the ratio of large plasmoids to small ones and help determine how much reconnection occurs.

Plasmoid reconnection resembles the that occurs in smart phones or in high-powered computers that model the weather. During this computing, many processors are calculating simultaneously and making the overall calculation rate quicker. Similarly, plasmoids speed up the overall rate of reconnection by making it occur in many places at once.

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By studying the risk of re-identification more thoroughly, researchers were able to better articulate the fundamental requirements for information to be anonymous. They realized that a robust definition of anonymous should not rely on what side information may be available to an attacker. This led to the definition of Differential Privacy in 2006 by Cynthia Dwork, then a researcher at Microsoft. It quickly became the gold standard for privacy and has been used in global technology products like Chrome, the iPhone, and Linkedin. Even the US Census used it for the 2020 census.

Differential privacy solves the problem of side information by looking at the most powerful attacker possible: an attacker who knows everything about everyone in a population except for a single individual. Let’s call her Alice. When releasing information to such an attacker, how can you protect Alice’s privacy? If you release exact aggregate information for the whole population (e.g., the average age of the population), the attacker can compute the difference between what you shared and the expected value of the aggregate with everyone but Alice. You just revealed something personal about Alice.

The only way out is to not share the exact aggregate information but add a bit of random noise to it and only share the slightly noisy aggregate information. Even for the most well-informed of attackers, differential privacy makes it impossible to deduce what value Alice contributed. Also, note that we have talked about simple insights like aggregations and averages but the same possibilities for re-identification apply to more sophisticated insights like machine learning or AI models, and the same differential privacy techniques can be used to protect privacy by adding noise when training models. Now, we have the right tools to find the optimal tradeoff: adding more noise makes it harder for a would-be attacker to re-identify Alice’s information, but at a greater loss of data fidelity for the data analyst. Fortunately, in practice, there is a natural alignment between differential privacy and statistical significance.

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