Europe’s new Digital Markets Act aims to make larger messaging platforms ‘interoperable’ with smaller ones. No wonder the tech titans are running scared.
Category: encryption – Page 30
Comparative analysis of genome code complexity and manufacturability with engineering benchmarks
When knowledge has advanced to a state that includes a predictive understanding of the relationship between genome sequence and organism phenotype it will be possible for future engineers to design and produce synthetic organisms. However, the possibility of synthetic biology does not necessarily guarantee its feasibility, in much the same way that the possibility of a brute force attack fails to ensure the timely breaking of robust encryption. The size and range of natural genomes, from a few million base pairs for bacteria to over 100 billion base pairs for some plants, suggests it is necessary to evaluate the practical limits of designing genomes of similar complexity.
Researchers show they can steal data during homomorphic encryption
Homomorphic encryption is considered a next generation data security technology, but researchers have identified a vulnerability that allows them to steal data even as it is being encrypted.
“We weren’t able to crack homomorphic encryption using mathematical tools,” says Aydin Aysu, senior author of a paper on the work and an assistant professor of computer engineering at North Carolina State University. “Instead, we used side-channel attacks. Basically, by monitoring power consumption in a device that is encoding data for homomorphic encryption, we are able to read the data as it is being encrypted. This demonstrates that even next generation encryption technologies need protection against side-channel attacks.”
Homomorphic encryption is a way of encrypting data so that third parties cannot read it. However, homomorphic encryption still allows third parties and third-party technologies to conduct operations using the data. For example, a user could use homomorphic encryption to upload sensitive data to a cloud computing system in order to perform analyses of the data. Programs in the cloud could perform the analyses and send the resulting information back to the user, but those programs would never actually be able to read the sensitive data.
Quantum Computers Could Crack Bitcoin. Here’s What It Would Take
Quantum computers could cause unprecedented disruption in both good and bad ways, from cracking the encryption that secures our data to solving some of chemistry’s most intractable puzzles. New research has given us more clarity about when that might happen.
Modern encryption schemes rely on fiendishly difficult math problems that would take even the largest supercomputers centuries to crack. But the unique capabilities of a quantum computer mean that at sufficient size and power these problems become simple, rendering today’s encryption useless.
That’s a big problem for cybersecurity, and it also poses a major challenge for cryptocurrencies, which use cryptographic keys to secure transactions. If someone could crack the underlying encryption scheme used by Bitcoin, for instance, they would be able to falsify these keys and alter transactions to steal coins or carry out other fraudulent activity.
Meta Is Making a Monster AI Supercomputer for the Metaverse
Though Meta didn’t give numbers on RSC’s current top speed, in terms of raw processing power it appears comparable to the Perlmutter supercomputer, ranked fifth fastest in the world. At the moment, RSC runs on 6,800 NVIDIA A100 graphics processing units (GPUs), a specialized chip once limited to gaming but now used more widely, especially in AI. Already, the machine is processing computer vision workflows 20 times faster and large language models (like, GPT-3) 3 times faster. The more quickly a company can train models, the more it can complete and further improve in any given year.
In addition to pure speed, RSC will give Meta the ability to train algorithms on its massive hoard of user data. In a blog post, the company said that they previously trained AI on public, open-source datasets, but RSC will use real-world, user-generated data from Meta’s production servers. This detail may make more than a few people blanch, given the numerous privacy and security controversies Meta has faced in recent years. In the post, the company took pains to note the data will be carefully anonymized and encrypted end-to-end. And, they said, RSC won’t have any direct connection to the larger internet.
To accommodate Meta’s enormous training data sets and further increase training speed, the installation will grow to include 16,000 GPUs and an exabyte of storage—equivalent to 36,000 years of high-quality video—later this year. Once complete, Meta says RSC will serve training data at 16 terabytes per second and operate at a top speed of 5 exaflops.