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privacy

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first_img OpenAI is facing a class-action lawsuit, accused of allowing outsourced personnel to read ChatGPT conversations

Two ChatGPT users from California filed a proposed class action lawsuit against OpenAI in the United States District Court for the Northern District of California this month, accusing the company of failing to adequately inform users that their real conversations were being handed over to external contractors for processing. The lawsuit was served to OpenAI on September 2, focusing on its internal initiative Project Lily. According to the complaint, "AI data reviewers" and "chatbot evaluators" recruited through a third-party staffing company read real ChatGPT prompts and complete conversations, summarize user intent, and score and comment on responses from four models on a scale of 1 to 7.This process is known in the industry as reinforcement learning from human feedback (RLHF), which is a fundamental method for enhancing chatbot capabilities. The complaint states that users were never explicitly informed that a person, rather than a machine, might be reading their conversations. OpenAI filters conversations through an automated system before human review, but the complaint alleges that the filters cannot intercept all content, and personal details sometimes still reach contractors. 404 Media first reported on the project on September 14 and found that the reviewers' dashboard included "user memory summaries," which could expose users' approximate locations, occupations, or private life information, even though usernames had been removed.OpenAI stated that such reviews aim to reduce two behaviors: chatbots behaving too much like humans and overly catering to users, referred to by researchers as "flattery." The complaint raises eight legal claims, including violations of California's Unfair Competition Law, Consumer Privacy Act, and common law claims for intrusion into private affairs, with the plaintiffs seeking damages, restitution of unjust enrichment, and punitive damages.

first_img Meta's Muse AI assistant reads users' private information and falsely reports the method of access

Meta's personal AI assistant Muse has been accused of reading users' private iMessages without permission and providing false explanations about how it accesses this information. Technology columnist Jason Aten installed the assistant on his iPhone and Mac when Muse launched on September 8 and explicitly refused to grant it access to personal data such as information and calendars.A few days later, Muse sent Aten a notification suggesting he write a column about the new iPhone that he had just discussed with his podcast partner, also mentioning a deadline reminder from the editor. Aten asked Muse how it knew this, and it replied that the Mac application only forwards notification previews and does not involve the content of messages. In reality, Muse had synchronized over 187,000 records from the local private information database on the Mac, an operation that requires full disk access permissions on macOS.David Singleton, head of Meta's superintelligent lab, responded on Threads that this feature requires users to actively choose to opt-in. Aten denied ever turning on that switch and stated that Meta had not answered related questions. Amazon has banned Muse from shopping on its site, stating that the assistant did not indicate its AI identity while browsing and seemed capable of capturing and storing user credentials. Since its launch, Muse has been downloaded over 2.5 million times, with its promotion emphasizing user control over access.

Meta rolled back Muse for phone call testing, with some calls completed by real people

According to internal information obtained by 404 Media and Reuters, after Meta expanded the Muse "Call for You" testing, it was revealed that some calls were actually completed by real people. Muse can transfer tasks to real contractors at call centers, who then make calls on behalf of users and fulfill requests. Some testers only realized there was human intervention after the call ended.Meta adopted this approach mainly to improve success rates. Internal testing showed that the success rate of phone tasks could reach 95% to 98% with human intervention, higher than pure AI. Some merchants hang up directly when they hear the other party is AI, and there are also testers in the community who reported that merchants hung up after Muse identified itself as an AI assistant.Human intervention raises privacy concerns, as users originally believed their information was only shared with AI, but it may actually be seen by outsourced personnel. Some people within Meta also questioned that providing privacy training to contractors does not equate to a real safety mechanism. A Meta official later admitted that failing to inform testers in advance about human intervention was a mistake, and this feature has been rolled back. Meta stated that the phone function is still in testing and will only be officially launched after ensuring safety, privacy, and clear communication.

Vitalik: Blockchain has evolved from a simple ledger into a cryptography-based secure computing network

Vitalik Buterin, co-founder of Ethereum, delivered a keynote speech at the 12th Global Blockchain Summit, stating that the blockchain was initially a primary tool. Although it has characteristics such as openness, neutrality, and security, it is not scalable and is completely transparent, lacking privacy protection mechanisms.Subsequently, privacy transactions and scalable verification brought by zero-knowledge proofs have changed this situation and have been transformed from planning into actual EIPs, included in the Ethereum roadmap. Technologies such as fully homomorphic encryption are also beginning to be applied, and indistinguishable obfuscation may also be applied on-chain in the future. Beyond privacy, the parallel construction and proof of blocks can make extreme scalability possible, and industry thinking has shifted from digital assets to digital assets plus information, from purely on-chain to user-end, transaction memory pools, a complete pipeline on-chain, and from who can send what to who can see what.Currently, blockchain has gradually evolved from a simple ledger to a cryptography-based secure computing network. With the development of AI, providing cryptographic rules to AI for automated formal verification can significantly enhance verification efficiency, allowing the chain to carry more possibilities.

first_img The Zcash community overwhelmingly voted to accelerate transactions and maintain Bitcoin-style halving

Zcash holders voted overwhelmingly to support speeding up transaction times while retaining the Bitcoin-like halving mechanism. This governance vote was for Zcash's next major network upgrade, NU7, with nearly 2.4 million ZEC participating in the vote, accounting for about two-thirds of the approximately 3.6 million eligible ZEC at the time of the snapshot. The results were calculated based on ZEC weight, with each eligible ZEC representing one vote.Among them, 99.9% of participating ZEC supported reducing the block time from 75 seconds to 25 seconds, 98.9% supported retaining the Bitcoin-like halving mechanism, 96.6% supported postponing the fee reissuance of the Network Sustainability Mechanism (NSM) until February 20231, and 99.3% supported launching NU7 as soon as possible, giving up any unrealized features before the deadline of September 30.This vote was limited to holders of ZEC in Zcash's latest privacy transaction pool, Ironwood. The ballots were encrypted and split into 16 parts for counting, making it impossible for validators to reconstruct the specific holders' votes and balances. Previously, a sentiment survey in February for NU7 only attracted 7.25% of circulating ZEC to participate. The voting results themselves will not directly change the network; the relevant features still need to be implemented, tested, and included in the final upgrade.
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