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Storing Encrypted Photos in Google’s Cloud

New paper: “Encrypted Cloud Photo Storage Using Google Photos“:

Abstract: Cloud photo services are widely used for persistent, convenient, and often free photo storage, which is especially useful for mobile devices. As users store more and more photos in the cloud, significant privacy concerns arise because even a single compromise of a user’s credentials give attackers unfettered access to all of the user’s photos. We have created Easy Secure Photos (ESP) to enable users to protect their photos on cloud photo services such as Google Photos. ESP introduces a new client-side encryption architecture that includes a novel format-preserving image encryption algorithm, an encrypted thumbnail display mechanism, and a usable key management system. ESP encrypts image data such that the result is still a standard format image like JPEG that is compatible with cloud photo services. ESP efficiently generates and displays encrypted thumbnails for fast and easy browsing of photo galleries from trusted user devices. ESP’s key management makes it simple to authorize multiple user devices to view encrypted image content via a process similar to device pairing, but using the cloud photo service as a QR code communication channel. We have implemented ESP in a popular Android photos app for use with Google Photos and demonstrate that it is easy to use and provides encryption functionality transparently to users, maintains good interactive performance and image quality while providing strong privacy guarantees, and retains the sharing and storage benefits of Google Photos without any changes to the cloud service

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Hiding Malware in ML Models

Interesting research: “EvilModel: Hiding Malware Inside of Neural Network Models”.

Abstract: Delivering malware covertly and detection-evadingly is critical to advanced malware campaigns. In this paper, we present a method that delivers malware covertly and detection-evadingly through neural network models. Neural network models are poorly explainable and have a good generalization ability. By embedding malware into the neurons, malware can be delivered covertly with minor or even no impact on the performance of neural networks. Meanwhile, since the structure of the neural network models remains unchanged, they can pass the security scan of antivirus engines. Experiments show that 36.9MB of malware can be embedded into a 178MB-AlexNet model within 1% accuracy loss, and no suspicious are raised by antivirus engines in VirusTotal, which verifies the feasibility of this method. With the widespread application of artificial intelligence, utilizing neural networks becomes a forwarding trend of malware. We hope this work could provide a referenceable scenario for the defense on neural network-assisted attacks.

News article.

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Insurance and Ransomware

As ransomware becomes more common, I’m seeing more discussions about the ethics of paying the ransom. Here’s one more contribution to that issue: a research paper that the insurance industry is hurting more than it’s helping.

However, the most pressing challenge currently facing the industry is ransomware. Although it is a societal problem, cyber insurers have received considerable criticism for facilitating ransom payments to cybercriminals. These add fuel to the fire by incentivising cybercriminals’ engagement in ransomware operations and enabling existing operators to invest in and expand their capabilities. Growing losses from ransomware attacks have also emphasised that the current reality is not sustainable for insurers either.

To overcome these challenges and champion the positive effects of cyber insurance, this paper calls for a series of interventions from government and industry. Some in the industry favour allowing the market to mature on its own, but it will not be possible to rely on changing market forces alone. To date, the UK government has taken a light-touch approach to the cyber insurance industry. With the market undergoing changes amid growing losses, more coordinated action by government and regulators is necessary to help the industry reach its full potential.

The interventions recommended here are still relatively light, and reflect the fact that cyber insurance is only a potential incentive for managing societal cyber risk.They include: developing guidance for minimum security standards for underwriting; expanding data collection and data sharing; mandating cyber insurance for government suppliers; and creating a new collaborative approach between insurers and intelligence and law enforcement agencies around ransomware.

Finally, although a well-functioning cyber insurance industry could improve cyber security practices on a societal scale, it is not a silver bullet for the cyber security challenge. It is important to remember that the primary purpose of cyber insurance is not to improve cyber security, but to transfer residual risk. As such, it should be one of many tools that governments and businesses can draw on to manage cyber risk more effectively.

Basically, the insurance industry incents companies to do the cheapest mitigation possible. Often, that’s paying the ransom.

News article.

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Intentional Flaw in GPRS Encryption Algorithm GEA-1

General Packet Radio Service (GPRS) is a mobile data standard that was widely used in the early 2000s. The first encryption algorithm for that standard was GEA-1, a stream cipher built on three linear-feedback shift registers and a non-linear combining function. Although the algorithm has a 64-bit key, the effective key length is only 40 bits, due to “an exceptional interaction of the deployed LFSRs and the key initialization, which is highly unlikely to occur by chance.”

GEA-1 was designed by the European Telecommunications Standards Institute in 1998. ETSI was — and maybe still is — under the auspices of SOGIS: the Senior Officials Group, Information Systems Security. That’s basically the intelligence agencies of the EU countries.

Details are in the paper: “Cryptanalysis of the GPRS Encryption Algorithms GEA-1 and GEA-2.” GEA-2 does not have the same flaw, although the researchers found a practical attack with enough keystream.

Hacker News thread.

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Detecting Deepfake Picture Editing

“Markpainting” is a clever technique to watermark photos in such a way that makes it easier to detect ML-based manipulation:

An image owner can modify their image in subtle ways which are not themselves very visible, but will sabotage any attempt to inpaint it by adding visible information determined in advance by the markpainter.

One application is tamper-resistant marks. For example, a photo agency that makes stock photos available on its website with copyright watermarks can markpaint them in such a way that anyone using common editing software to remove a watermark will fail; the copyright mark will be markpainted right back. So watermarks can be made a lot more robust.

Here’s the paper: “Markpainting: Adversarial Machine Learning Meets Inpainting,” by David Khachaturov, Ilia Shumailov, Yiren Zhao, Nicolas Papernot, and Ross Anderson.

Abstract: Inpainting is a learned interpolation technique that is based on generative modeling and used to populate masked or missing pieces in an image; it has wide applications in picture editing and retouching. Recently, inpainting started being used for watermark removal, raising concerns. In this paper we study how to manipulate it using our markpainting technique. First, we show how an image owner with access to an inpainting model can augment their image in such a way that any attempt to edit it using that model will add arbitrary visible information. We find that we can target multiple different models simultaneously with our technique. This can be designed to reconstitute a watermark if the editor had been trying to remove it. Second, we show that our markpainting technique is transferable to models that have different architectures or were trained on different datasets, so watermarks created using it are difficult for adversaries to remove. Markpainting is novel and can be used as a manipulation alarm that becomes visible in the event of inpainting.

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Information Flows and Democracy

Henry Farrell and I published a paper on fixing American democracy: “Rechanneling Beliefs: How Information Flows Hinder or Help Democracy.”

It’s much easier for democratic stability to break down than most people realize, but this doesn’t mean we must despair over the future. It’s possible, though very difficult, to back away from our current situation towards one of greater democratic stability. This wouldn’t entail a restoration of a previous status quo. Instead, it would recognize that the status quo was less stable than it seemed, and a major source of the tensions that have started to unravel it. What we need is a dynamic stability, one that incorporates new forces into American democracy rather than trying to deny or quash them.

This paper is our attempt to explain what this might mean in practice. We start by analyzing the problem and explaining more precisely why a breakdown in public consensus harms democracy. We then look at how these beliefs are being undermined by three feedback loops, in which anti-democratic actions and anti-democratic beliefs feed on each other. Finally, we explain how these feedback loops might be redirected so as to sustain democracy rather than undermining it.

To be clear: redirecting these and other energies in more constructive ways presents enormous challenges, and any plausible success will at best be untidy and provisional. But, almost by definition, that’s true of any successful democratic reforms where people of different beliefs and values need to figure out how to coexist. Even when it’s working well, democracy is messy. Solutions to democratic breakdowns are going to be messy as well.

This is part of our series of papers looking at democracy as an information system. The first paper was “Common-Knowledge Attacks on Democracy.”

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New Spectre-Like Attacks

There’s new research that demonstrates security vulnerabilities in all of the AMD and Intel chips with micro-op caches, including the ones that were specifically engineered to be resistant to the Spectre/Meltdown attacks of three years ago.


The new line of attacks exploits the micro-op cache: an on-chip structure that speeds up computing by storing simple commands and allowing the processor to fetch them quickly and early in the speculative execution process, as the team explains in a writeup from the University of Virginia. Even though the processor quickly realizes its mistake and does a U-turn to go down the right path, attackers can get at the private data while the processor is still heading in the wrong direction.

It seems really difficult to exploit these vulnerabilities. We’ll need some more analysis before we understand what we have to patch and how.

More news.

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Wi-Fi Devices as Physical Object Sensors

The new 802.11bf standard will turn Wi-Fi devices into object sensors:

In three years or so, the Wi-Fi specification is scheduled to get an upgrade that will turn wireless devices into sensors capable of gathering data about the people and objects bathed in their signals.

“When 802.11bf will be finalized and introduced as an IEEE standard in September 2024, Wi-Fi will cease to be a communication-only standard and will legitimately become a full-fledged sensing paradigm,” explains Francesco Restuccia, assistant professor of electrical and computer engineering at Northeastern University, in a paper summarizing the state of the Wi-Fi Sensing project (SENS) currently being developed by the Institute of Electrical and Electronics Engineers (IEEE).

SENS is envisioned as a way for devices capable of sending and receiving wireless data to use Wi-Fi signal interference differences to measure the range, velocity, direction, motion, presence, and proximity of people and objects.

More detail in the article. Security and privacy controls are still to be worked out, which means that there probably won’t be any.

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Determining Key Shape from Sound

It’s not yet very accurate or practical, but under ideal conditions it is possible to figure out the shape of a house key by listening to it being used.

Listen to Your Key: Towards Acoustics-based Physical Key Inference

Abstract: Physical locks are one of the most prevalent mechanisms for securing objects such as doors. While many of these locks are vulnerable to lock-picking, they are still widely used as lock-picking requires specific training with tailored instruments, and easily raises suspicion. In this paper, we propose SpiKey, a novel attack that significantly lowers the bar for an attacker as opposed to the lock-picking attack, by requiring only the use of a smartphone microphone to infer the shape of victim’s key, namely bittings(or cut depths) which form the secret of a key. When a victim inserts his/her key into the lock, the emitted sound is captured by the attacker’s microphone.SpiKey leverages the time difference between audible clicks to ultimately infer the bitting information, i.e., shape of the physical key. As a proof-of-concept, we provide a simulation, based on real-world recordings, and demonstrate a significant reduction in search spacefrom a pool of more than 330 thousand keys to three candidate keys for the most frequent case.

Scientific American podcast:

The strategy is a long way from being viable in the real world. For one thing, the method relies on the key being inserted at a constant speed. And the audio element also poses challenges like background noise.

Boing Boing post.

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