This reliance on cloud computing poses significant security risks, particularly in areas like health care, where hospitals may be hesitant to use AI tools to analyze confidential patient data due to privacy concerns.

To tackle this pressing issue, MIT researchers have developed a security protocol that leverages the quantum properties of light to guarantee that data sent to and from a cloud server remain secure during deep-learning computations.

By encoding data into the laser light used in fiber optic communications systems, the protocol exploits the fundamental principles of quantum mechanics, making it impossible for attackers to copy or intercept the information without detection.

Moreover, the technique guarantees security without compromising the accuracy of the deep-learning models. In tests, the researcher demonstrated that their protocol could maintain 96 percent accuracy while ensuring robust security measures.

“Deep learning models like GPT-4 have unprecedented capabilities but require massive computational resources. Our protocol enables users to harness these powerful models without compromising the privacy of their data or the proprietary nature of the models themselves,” says Kfir Sulimany, an MIT postdoc in the Research Laboratory for Electronics (RLE) and lead author of a paper on this security protocol.

Sulimany is joined on the paper by Sri Krishna Vadlamani, an MIT postdoc; Ryan Hamerly, a former postdoc now at NTT Research, Inc.; Prahlad Iyengar, an electrical engineering and computer science (EECS) graduate student; and senior author Dirk Englund, a professor in EECS, principal investigator of the Quantum Photonics and Artificial Intelligence Group and of RLE. The research was recently presented at Annual Conference on Quantum Cryptography.

A two-way street for security in deep learning

The cloud-based computation scenario the researchers focused on involves two parties — a client that has confidential data, like medical images, and a central server that controls a deep learning model.

The client wants to use the deep-learning model to make a prediction, such as whether a patient has cancer based on medical images, without revealing information about the patient.

In this scenario, sensitive data must be sent to generate a prediction. However, during the process the patient data must remain secure.

Also, the server does not want to reveal any parts of the proprietary model that a company like OpenAI spent years and millions of dollars building.

“Both parties have something they want to hide,” adds Vadlamani.

In digital computation, a bad actor could easily copy the data sent from the server or the client.

Quantum information, on the other hand, cannot be perfectly copied. The researchers leverage this property, known as the no-cloning principle, in their security protocol.

For the researchers’ protocol, the server encodes the weights of a deep neural network into an optical field using laser light.

A neural network is a deep-learning model that consists of layers of interconnected nodes, or neurons, that perform computation on data. The weights are the components of the model that do the mathematical operations on each input, one layer at a time. The output of one layer is fed into the next layer until the final layer generates a prediction.

The server transmits the network’s weights to the client, which implements operations to get a result based on their private data. The data remain shielded from the server.

At the same time, the security protocol allows the client to measure only one result, and it prevents the client from copying the weights because of the quantum nature of light.

Once the client feeds the first result into the next layer, the protocol is designed to cancel out the first layer so the client can’t learn anything else about the model.

“Instead of measuring all the incoming light from the server, the client only measures the light that is necessary to run the deep neural network and feed the result into the next layer. Then the client sends the residual light back to the server for security checks,” Sulimany explains.

Due to the no-cloning theorem, the client unavoidably applies tiny errors to the model while measuring its result. When the server receives the residual light from the client, the server can measure these errors to determine if any information was leaked. Importantly, this residual light is proven to not reveal the client data.

A practical protocol

Modern telecommunications equipment typically relies on optical fibers to transfer information because of the need to support massive bandwidth over long distances. Because this equipment already incorporates optical lasers, the researchers can encode data into light for their security protocol without any special hardware.

When they tested their approach, the researchers found that it could guarantee security for server and client while enabling the deep neural network to achieve 96 percent accuracy.

The tiny bit of information about the model that leaks when the client performs operations amounts to less than 10 percent of what an adversary would need to recover any hidden information. Working in the other direction, a malicious server could only obtain about 1 percent of the information it would need to steal the client’s data.

“You can be guaranteed that it is secure in both ways — from the client to the server and from the server to the client,” Sulimany says.

“A few years ago, when we developed our demonstration of distributed machine learning inference between MIT’s main campus and MIT Lincoln Laboratory, it dawned on me that we could do something entirely new to provide physical-layer security, building on years of quantum cryptography work that had also been shown on that testbed,” says Englund. “However, there were many deep theoretical challenges that had to be overcome to see if this prospect of privacy-guaranteed distributed machine learning could be realized. This didn’t become possible until Kfir joined our team, as Kfir uniquely understood the experimental as well as theory components to develop the unified framework underpinning this work.”

In the future, the researchers want to study how this protocol could be applied to a technique called federated learning, where multiple parties use their data to train a central deep-learning model. It could also be used in quantum operations, rather than the classical operations they studied for this work, which could provide advantages in both accuracy and security.

This work was supported, in part, by the Israeli Council for Higher Education and the Zuckerman STEM Leadership Program.

According to their new study in eBioMedicine, these mouse models are important for COVID-19 research because their cells were engineered to include two important human molecules that are involved in SARS-CoV-2 infection of human cells — and these humanized mice were generated on two different immunologic backgrounds. The new models can help shed light on how SARS-CoV-2 moves through the body and why different people experience wildly different COVID-19 symptoms.

“With these mouse models, we can model epidemiologically-relevant SARS-CoV-2 infection and vaccination settings, and we can study all relevant tissues (not just the blood) at different timepoints following infection and/or vaccination,” says LJI Professor Sujan Shresta, Ph.D., who co-led the research with LJI Histopathology Core Director Kenneth Kim, Dipl. ACVP, and the late Kurt Jarnagin, Ph.D., of Synbal, Inc.

Already, these new mouse models have helped scientists capture a clearer picture of how SARS-CoV-2 affects humans. They are also available to the wider COVID-19 research community.

“This work is part of LJI’s mission to contribute to pandemic preparedness around the world,” says Shresta.

Mouse models are a critical tool for understanding infection

Shresta’s lab is known for producing mouse models to study immune responses to infectious diseases such as dengue virus and Zika virus. In 2021, her laboratory partnered with Synbal, Inc., a preclinical biotechnology company based in San Diego, CA, to develop multi-gene, humanized mouse models for COVID-19 research. The project was also supported by Synbal CEO and LJI Board Member David R. Webb, Ph.D.

Shresta and Jarnagin collaborated to produce mice that express either human ACE2, human TMPRSS2, or both molecules in the C57BL/6 and BALB/c mouse genetic backgrounds. “Immunologists have found that these two genetic backgrounds in mice elicit different immune responses,” says Shresta.

As Shresta explains, having the flexibility to include the genes for one or both of these molecules in two different mouse genetic backgrounds gives scientists an opportunity to investigate two key areas. First, they can examine how each of these molecules contribute to infection with different SARS-CoV-2 variants. Second, they can study how the host’s genetic background might influence disease progression and immune response following infection with different variants.

Zooming into infected tissues

The researchers then took a closer look at how these models responded to actual SARS-CoV-2 infection. LJI Postdoctoral Fellow Shailendra Verma, Ph.D., worked in LJI’s High Containment (BSL-3) Facility to take tissue samples from the various mouse strains exposed to SARS-CoV-2.

“This work wouldn’t have been possible if we didn’t have a BSL-3 facility at LJI,” says Shresta, who has worked closely with LJI’s Department of Environmental Health and Safety to conduct several cutting-edge studies in the facility.

Next, Kim, a board-certified pathologist, examined the tissue samples and compared them to pathologic findings from humans with COVID-19.

Kim’s analysis showed signs of SARS-CoV-2 infection in the lungs, which are also the tissue most vulnerable to SARS-CoV-2 infection in humans. Kim could also see mouse immune cells responding to infection in a way that reflected the human immune response.

By characterizing these responses in the new mouse models, the researchers have established a foundation for understanding the immune heterogeneity — or wide range of immune responses — of SARS-CoV-2-induced disease.

“There’s no such thing as a perfect animal model, but our goal is always to make an animal model that recapitulates the human disease and immune response as much as possible,” says Shresta.

The new mouse models may prove valuable for studying responses to emerging SARS-CoV-2 variants and future coronaviruses with pandemic potential.

“Not only are these models useful for current COVID-19 studies, but if there should be another coronavirus pandemic — with a virus that utilizes the same ACE2 receptor and/or TMPRSS2 molecule for viral entry into human cells — then these mouse lines on two different genetic backgrounds will be ready,” says Kim.

Additional authors of the study, “Influence of Th1 versus Th2 immune bias on viral, pathological, and immunological dynamics in SARS-CoV-2 variant-infected human ACE2 knock-in mice,” include Erin Maule, Paolla B. A. Pinto, Chris Conner, Kristen Valentine, Dale O Cowley, Robyn Miller, Annie Elong Ngono, Linda Tran, Krithik Varghese, Rúbens Prince dos Santos Alves, Kathryn M. Hastie and Erica Ollmann Saphire.

This study was supported by the National Institutes of Health (grant U19 AI142790-02S1 and R44 AI157900), the GHR Foundation, the Arvin Gottlieb Charitable Foundation, the Overton family, and by a American Association of Immunologists Career Reentry Fellowship (FASB).

Dogs respond to human speech, even though they themselves cannot produce human sounds. To better understand how people and pups communicate, the scientists analyzed the vocal sounds of 30 dogs. They also analyzed the sounds of 27 humans across five languages speaking to other people, and 22 humans across those languages speaking to dogs. The scientists also used electroencephalography (EEG) to examine the brain responses to speech in humans and dogs.

Humans are much faster ‘talkers’ than dogs, the study showed, with a speech rate of about four syllables per second, while dogs bark, growl, woof, and whine at a rate of about two vocalizations per second. When talking to dogs, the humans slowed their speech to around three syllables per second. EEG signals of humans and canines showed that dogs’ neural responses to speech are focused on delta rhythms, while human responses to speech are focused on faster theta rhythms. The authors suggest that humans and dogs have different vocal processing systems, and that slowing down our speech when speaking to pets may have ultimately helped us better connect with them.

The authors add, “What’s further interesting, is that while dogs use slow rhythm to process speech and contrary to popular beliefs, they need both content and prosody to successfully comprehend it.”

The research also found that drinking more than four cups of coffee per day also increases the risk of stroke.

The findings come from two analyses of the INTERSTROKE research project which have been published — the effects of fizzy drinks, fruit juice/drink and water was reported in the Journal of Stroke; and the findings related to tea and coffee in the International Journal of Stroke.

Stroke occurs when the blood supply to part of the brain is cut-off and damages brain cells — it can either be ischemic stroke, which is usually due to a blood clot, or when there intracerebral haemorrhage, which is bleeding into the brain tissue.

INTERSTROKE is one of the largest international studies of risk factors for stroke, involving almost 27,000 people, in 27 countries, including almost 13,500 people who experienced their first stroke.

Those who took part in the study came from a broad range of geographical and ethnic backgrounds, with different cardiovascular risk profiles, including Ireland and the UK.

The study which focused on people’s consumption of fizzy drinks and fruit juice found:

• Fizzy drinks, including both sugar-sweetened and artificially sweetened such as diet or zero sugar, were linked with a 22% increased chance of stroke, and the risk increased sharply with two or more of these drinks a day
• The link between fizzy drinks and chance of stroke was greatest in Eastern/Central Europe and Middle East, Africa, and South America
• The research noted that many products marketed as fruit juice are made from concentrates and contain added sugars and preservatives, which may offset the benefits usually linked with fresh fruit, and actually increase stroke risk
• Fruit juice drinks were linked with a 37% increase in chance of stroke due to bleeding (intracranial haemorrhage). With two of these drinks a day, the risk triples
• Women show the greatest increased chance of stroke due to bleeding (intracranial haemorrhage) linked to fruit juice/drinks
• Drinking more than 7 cups of water a day was linked with a reduced odds of stroke caused by a clot

Lead researcher on both studies Professor Andrew Smyth, Professor of Clinical Epidemiology at University of Galway and Consultant Physician at Galway University Hospitals, said: “Not all fruit drinks are created equal — freshly squeezed fruit juices are most likely to bring benefits, but fruit drinks made from concentrates, with lots of added sugars and preservatives, may be harmful. Our research also shows that the chance of stroke increases the more often someone consumes fizzy drinks.

“As a doctor and as someone who has researched the risk of stroke, we would encourage people to avoid or minimise their consumption of fizzy and fruit drinks, and to consider switching to water instead.”

The study which focused on people’s consumption of coffee and tea found:

• Drinking more than four cups of coffee a day increased chance of stroke by 37%, but not associated with stroke risk for lower intakes
• Drinking tea was linked with a reduced chance of stroke by 18-20%
• Drinking 3-4 cups per day of black tea — including Breakfast and Earl Grey teas, but not green tea or herbal teas — was linked with a 29% lower chance of stroke
• Drinking 3-4 cups per day of green tea was linked with a 27% lower chance of stroke
• Adding milk may reduce or block the beneficial effects of antioxidants that can be found in tea. The reduced chance of stroke from drinking tea was lost for those that added milk
• There were important geographical differences in the findings — tea was linked with lower chance of stroke in China and South America but higher chance of stroke in South Asia

Professor Martin O’Donnell, Executive Dean of College of Medicine, Nursing and Health Sciences at University of Galway and Consultant Stroke Physician at Galway University Hospitals, co-leads the INTERSTROKE study in partnership with Professor Salim Yusuf of the Population Health Research Institute at McMaster University, Canada.

Professor O’Donnell said: “A key goal of the INTERSTROKE study is to provide usable information on how to reduce one’s risk of stroke. While hypertension is the most important risk factor, our stroke risk can also be lowered through healthy lifestyle choices in diet and physical activity. The current study adds further information on what constitutes healthy choices on daily intake of beverages.”

In Medieval Europe and the Middle East, merchants from faraway places traded in rare, precious commodities. One of the most popular commodities was ivory, which came from places few could have imagined.

Because when the Crusades temporarily prevented trade in ivory from elephants, walrus ivory became a popular commodity, and seeing as walruses only live in icy-cold parts of the world, it must have been brought to European markets from faraway places.

New research from the University of Copenhagen shows that the Vikings were (the first) part of a network that supplied not just Europe and the Middle East, but probably also East Asia with walrus ivory.

“Our study shows that the Vikings regularly travelled the around 6,000 kilometres to Pikialasorsuaq in Northwest Greenland, an area characterised by harsh climatic conditions. And they probably didn’t do it for the thrill of it, but to obtain this precious commodity, which they brought to Northern Europe and other parts of the world,” says Associate Professor Morten Tange Olsen from the Globe Institute at the University of Copenhagen, who is one of the authors of the new study.

The researchers conducted DNA analyses, which show that the Vikings covered a greater distance than previously believed in their search for walruses.

The study is a collaboration between researchers at the University of Copenhagen, the University of Lund in Sweden and the University of Groningen in the Netherlands and international partners from Greenland, Iceland and Canada.

Trade and exchange of hunting techniques

To prove that the Vikings did indeed cover such a great distance, the researchers studied fragments of walrus skulls mainly, obtained from excavations of Viking villages in Europe and settlements in Greenland and Canada.

“DNA sequences from these fragments provided us with a genetic map of the place of origin of various Arctic walrus populations at the time of the Vikings. This enabled us to show in which part of the Arctic the animals were caught,” says Morten Tange Olsen.

The study also demonstrates that the Vikings probably had more dealings with indigenous Arctic populations than previously assumed, including the Thule and Dorset cultures.

“Our research shows that the Vikings were extremely well-travelled and had a well-established network that covered a larger area than previously believed and which in time and place must have overlapped with early Greenlandic and Canadian cultures,” says Morten Tange Olsen, who is a marine mammal biologist and geneticist. He believes the cross-disciplinary collaboration between archaeologists, biologists and geneticists is what has made the study a success.

The new study once again shows that the Vikings had a remarkable ability to navigate and survive in harsh climatic conditions, and that they helped create a global trade network that reached beyond the borders of Europe.

“Now, for the first time ever, we have a clear genetic map of Arctic walrus populations, which tells us where the Norsemen went to obtain the precious commodity, ivory.”

Morten Tange Olsen and his colleagues hope the study will open our eyes to the Vikings’ complex and extensive trade network and interaction with other cultures.

The Princeton Plasma Physics Laboratory (PPPL) — a U.S. national laboratory funded by the Department of Energy (DOE) — is leading several efforts on this front, including collaborating on the design and development of a new fusion device at the University of Seville in Spain. The SMall Aspect Ratio Tokamak (SMART) strongly benefits from PPPL computer codes as well as the Lab’s expertise in magnetics and sensor systems.

“The SMART project is a great example of us all working together to solve the challenges presented by fusion and teaching the next generation what we have already learned,” said Jack Berkery, PPPL’s deputy director of research for the National Spherical Torus Experiment-Upgrade (NSTX-U) and principal investigator for the PPPL collaboration with SMART. “We have to all do this together or it’s not going to happen.”

Manuel Garcia-Munoz and Eleonora Viezzer, both professors at the Department of Atomic, Molecular and Nuclear Physics of the University of Seville as well as co-leaders of the Plasma Science and Fusion Technology Lab and the SMART tokamak project, said PPPL seemed like the ideal partner for their first tokamak experiment. The next step was deciding what kind of tokamak they should build. “It needed to be one that a university could afford but also one that could make a unique contribution to the fusion landscape at the university scale,” said Garcia-Munoz. “The idea was to put together technologies that were already established: a spherical tokamak and negative triangularity, making SMART the first of its kind. It turns out it was a fantastic idea.”

SMART should offer easy-to-manage fusion plasma

Triangularity refers to the shape of the plasma relative to the tokamak. The cross section of the plasma in a tokamak is typically shaped like the capital letter D. When the straight part of the D faces the center of the tokamak, it is said to have positive triangularity. When the curved part of the plasma faces the center, the plasma has negative triangularity.

Garcia-Munoz said negative triangularity should offer enhanced performance because it can suppress instabilities that expel particles and energy from the plasma, preventing damage to the tokamak wall. “It’s a potential game changer with attractive fusion performance and power handling for future compact fusion reactors,” he said. “Negative triangularity has a lower level of fluctuations inside the plasma, but it also has a larger divertor area to distribute the heat exhaust.”

The spherical shape of SMART should make it better at confining the plasma than it would be if it were doughnut shaped. The shape matters significantly in terms of plasma confinement. That is why NSTX-U, PPPL’s main fusion experiment, isn’t squat like some other tokamaks: the rounder shape makes it easier to confine the plasma. SMART will be the first spherical tokamak to fully explore the potential of a particular plasma shape known as negative triangularity.

PPPL’s expertise in computer codes proves essential

PPPL has a long history of leadership in spherical tokamak research. The University of Seville fusion team first contacted PPPL to implement SMART in TRANSP, a simulation software developed and maintained by the Lab. Dozens of facilities use TRANSP, including private ventures such as Tokamak Energy in England.

“PPPL is a world leader in many, many areas, including fusion simulation; TRANSP is a great example of their success,” said Garcia-Munoz.

Mario Podesta, formerly of PPPL, was integral to helping the University of Seville determine the configuration of the neutral beams used for heating the plasma. That work culminated in a paper published in the journal Plasma Physics and Controlled Fusion.

Stanley Kaye, director of research for NSTX-U, is now working with Diego Jose Cruz-Zabala, EUROfusion Bernard Bigot Researcher Fellow, from the SMART team, using TRANSP “to determine the shaping coil currents necessary for attaining their design plasma shapes of positive triangularity and negative triangularity at different phases of operation.” The first phase, Kaye said, will involve a “very basic” plasma. Phase two will have neutral beams heating the plasma.

Separately, other computer codes were used for assessing the stability of future SMART plasmas by Berkery, former undergraduate intern John Labbate, who is, now a grad student at Columbia University, and former University of Seville graduate student Jesús Domínguez-Palacios, who has now moved to an American company. A new paper in Nuclear Fusion by Domínguez-Palacios discusses this work.

Designing diagnostics for the long haul

The collaboration between SMART and PPPL also extended into and one of the Lab’s core areas of expertise: diagnostics, which are devices with sensors to assess the plasma. Several such diagnostics are being designed by PPPL researchers. PPPL Physicists Manjit Kaur and Ahmed Diallo, together with Viezzer, are leading the design of the SMART’s Thomson scattering diagnostic, for example. This diagnostic will precisely measure the plasma electron temperature and density during fusion reactions, as detailed in a new paper published in the journal Review of Scientific Instruments. These measurements will be complemented with ion temperature, rotation and density measurements provided by diagnostics known as the charge exchange recombination spectroscopy suite developed by Alfonso Rodriguez-Gonzalez, graduate student at University of Seville, Cruz-Zabala and Viezzer.

“These diagnostics can run for decades, so when we design the system, we keep that in mind,” said Kaur. When developing the designs, it was important the diagnostic can handle temperature ranges SMART might achieve in the next few decades and not just the initial, low values, she said.

Kaur designed the Thomson scattering diagnostic from the start of the project, selecting and procuring its different subparts, including the laser she felt best fits the job. She was thrilled to see how well the laser tests went when Gonzalo Jimenez and Viezzer sent her photos from Spain. The test involved setting up the laser on a bench and shooting it at a piece of special parchment that the researchers call “burn paper.” If the laser is designed just right, the burn marks will be circular with relatively smooth edges. “The initial laser test results were just gorgeous,” she said. “Now, we eagerly await receiving other parts to get the diagnostic up and running.”

James Clark, a PPPL research engineer whose doctoral thesis focused on Thomson scattering systems, was later brought on to work with Kaur. “I’ve been designing the laser path and related optics,” Clark explained. In addition to working on the engineering side of the project, Clark has also helped with logistics, deciding how and when things should be delivered, installed and calibrated.

PPPL’s Head of Advanced Projects Luis Delgado-Aparicio, together with Marie Skłodowska-Curie fellow Joaquin Galdon-Quiroga and University of Seville graduate student Jesus Salas-Barcenas, are leading efforts to add two other kinds of diagnostics to SMART: a multi-energy, soft X-ray (ME-SXR) diagnostic and spectrometers. The ME-SXR will also measure the plasma’s electron temperature and density but using a different approach than the Thomson scattering system. The ME-SXR will use sets of small electronic components called diodes to measure X-rays. Combined, the Thomson scattering diagnostic and the ME-SXR will comprehensively analyze the plasma’s electron temperature and density.

By looking at the different frequencies of light inside the tokamak, the spectrometers can provide information about impurities in the plasma, such as oxygen, carbon and nitrogen. “We are using off-the-shelf spectrometers and designing some tools to put them in the machine, incorporating some fiber optics,” Delgado-Aparicio said. Another new paper published in the Review of Scientific Instruments discusses the design of this diagnostic.

PPPL Research Physicist Stefano Munaretto worked on the magnetic diagnostic system for SMART with the field work led by University of Seville graduate student Fernando Puentes del Pozo Fernando. “The diagnostic itself is pretty simple,” said Munaretto. “It’s just a wire wound around something. Most of the work involves optimizing the sensor’s geometry by getting its size, shape and length correct, selecting where it should be located and all the signal conditioning and data analysis involved after that.” The design of SMART’s magnetics is detailed in a new paper.

Munaretto said working on SMART has been very fulfilling, with much of the team working on the magnetic diagnostics made up of young students with little previous experience in the field. “They are eager to learn, and they work a lot. I definitely see a bright future for them.”

Delgado-Aparicio agreed. “I enjoyed quite a lot working with Manuel Garcia-Munoz, Eleonora Viezzer and all of the other very seasoned scientists and professors at the University of Seville, but what I enjoyed most was working with the very vibrant pool of students they have there,” he said. “They are brilliant and have helped me quite a bit in understanding the challenges that we have and how to move forward toward obtaining first plasmas.”

Researchers at the University of Seville have already run a test in the tokamak, displaying the pink glow of argon when heated with microwaves. This process helps prepare the tokamak’s inner walls for a far denser plasma contained at a higher pressure. While technically, that pink glow is from a plasma, it’s at such a low pressure that the researchers don’t consider it their real first tokamak plasma. Garcia-Munoz says that will likely happen in the fall of 2024.

Support for this research comes from the DOE under contract number DE-AC02-09CH11466, European Research Council Grant Agreements 101142810 and 805162, the Euratom Research and Training Programme Grant Agreement 101052200 — EUROfusion, and the Junta de Andalucía Ayuda a Infraestructuras y Equipamiento de I+D+i IE17-5670 and Proyectos I+D+i FEDER Andalucía 2014-2020, US-15570.