Apple Announces New Machine Learning-led Child Safety Features

Apple Announces New Machine Learning-led Child Safety Features

Tech giant Apple has announced a range of new machine learning-led safety measures designed to protect children from exposure to child abuse materials, including child pornography.

The first of these is a new communication safety feature in Apple’s messages app, in which a warning will pop up when a child who is in an iCloud Family receives or attempts to send sexually explicit photos.

Any such images that are received by children be blurred, and a message will come up stating: “may be sensitive.” If the child then taps “view photo,” a different pop-up message will explain that if they choose to view the image, their iCloud Family parent will receive a notification “to make sure you’re OK.” The pop-up will also contain a link to receive additional help. A similar system is in place for sexually explicit photos a child tries to send.

An on-device machine learning system will analyze the image attachments to determine if a photo is sexually explicit. Apple also confirmed that iMessage remains end-to-end encrypted and that it will not have access to any of the messages.

The opt-in feature will be rolled out “later this year to accounts set up as families in iCloud for iOS 15, iPadOS 15, and macOS Monterey,” starting in the US.

The next measure enables Apple to detect child sexual abuse material (CSAM) stored in iCloud photos before reporting them to the National Center for Missing and Exploited Children (NCMEC). New technology in iOS and iPadOS will be used, enabling on-device matching utilizing a database of known CSAM image hashes provided by the NCMEC. This database is then transformed into an unreadable set of hashes securely stored on users’ devices.

Apple explained that the matching process is powered by a cryptographic technology called private set intersection, which determines if there is a match without revealing the result. In addition, there is a different technology, called threshold secret sharing, which aims to safeguard user privacy by ensuring the contents of the safety vouchers cannot be interpreted by Apple unless the iCloud Photos account crosses a threshold of known CSAM content.  

Apple stated: “This innovative new technology allows Apple to provide valuable and actionable information to NCMEC and law enforcement regarding the proliferation of known CSAM.”

The third new feature announced is the creation of additional resources in Siri and Search that offer advice to children and parents on staying safe online. Additionally, Apple will be updating Siri and Search to intervene when users perform searches for queries related to CSAM. “These interventions will explain to users that interest in this topic is harmful and problematic, and provide resources from partners to get help with this issue.”

This update will be rolled out later this year “in an update to iOS 15, iPadOS 15, watchOS 8, and macOS Monterey.”

Privacy campaigners have expressed concerns over the use of machine learning in these new features. Chris Hauk, consumer privacy champion at Pixel Privacy, commented: “While I am all for clamping down on child abuse and child pornography, I do have privacy concerns about the use of the technology. A machine learning system such as this could crank out false positives, leading to unwarranted issues for innocent citizens. Such technology could be abused if placed in government hands, leading to its use to detect images containing other types of content, such as photos taken at demonstrations and other types of gatherings. This could lead to the government clamping down on users’ freedom of expression and used to suppress “unapproved” opinions and activism.”  

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Millions of Senior Citizens’ Personal Data Exposed by Misconfiguration

Millions of Senior Citizens’ Personal Data Exposed by Misconfiguration

Millions of senior citizens in North America have had their personal information compromised following a breach at senior care review website SeniorAdvisor, according to WizCase.

The researchers, led by Ata Hakcil, discovered a misconfigured Amazon S3 bucket owned by Senior Advisor, a company that displays consumer ratings and reviews for senior care services across the US and Canada.

The misconfigured bucket left over the personal data of more than three million people, labeled “leads,” exposed. This included names, emails, phone numbers and dates contacted. In total, it contained more than one million files and 182GB of data, none of which was encrypted and did not require a password or login credentials to access.

WizCase believes the files are from 2002-2013 based on the contact dates, although the files were timestamped in 2017.

Additionally, the team found around 2000 “scrubbed” reviews in the misconfigured bucket, in which the user’s sensitive information was wiped or redacted. However, the scrubbed reviews contained a lead ID that would enable a malicious actor to trace it back to the person who wrote it as the reviews and lead data were in the same exposed database.

WizCase added that it reached out to SeniorAdvisor about its findings, who confirmed the breach had been secured. Nevertheless, the exposed data could be used to launch scams and phishing attempts, which is especially worrying in this case, given that senior citizens are at higher risk of online fraud than the rest of the population.

 “The greatest danger of this breach stems from the specific group of people left vulnerable. SeniorAdvisor is targeted toward senior citizens in or near retirement. In a 2018-2019 report, the FTC noted that people who filed a fraud complaint in the ages of 60-69 lost $600 per scam on average. The amount rose as the age group was older, culminating in $1700 on average per scam for people in the ages of 80-89,” outlined WizCase.

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Using “Master Faces” to Bypass Face-Recognition Authenticating Systems

Fascinating research: “Generating Master Faces for Dictionary Attacks with a Network-Assisted Latent Space Evolution.”

Abstract: A master face is a face image that passes face-based identity-authentication for a large portion of the population. These faces can be used to impersonate, with a high probability of success, any user, without having access to any user-information. We optimize these faces, by using an evolutionary algorithm in the latent embedding space of the StyleGAN face generator. Multiple evolutionary strategies are compared, and we propose a novel approach that employs a neural network in order to direct the search in the direction of promising samples, without adding fitness evaluations. The results we present demonstrate that it is possible to obtain a high coverage of the population (over 40%) with less than 10 master faces, for three leading deep face recognition systems.

Two good articles.

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