About 50% of the world’s chocolate originates from cacao trees in the West Africa countries of Ivory Coast and Ghana. The damaging virus is attacking cacao trees in Ghana, resulting in harvest losses of between 15 and 50%. Spread by small insects called mealybugs that eat the leaves, buds and flowers of trees, the cacao swollen shoot virus disease (CSSVD) is among the most damaging threats to the root ingredient of chocolate.

“This virus is a real threat to the global supply of chocolate,” said Benito Chen-Charpentier, professor of mathematics at The University of Texas at Arlington and an author of “Cacao sustainability: The case of cacao swollen-shoot virus co-infection” in the journal PLoS One. “Pesticides don’t work well against mealybugs, leaving farmers to try to prevent the spread of the disease by cutting out infected trees and breeding resistant trees. But despite these efforts, Ghana has lost more than 254 million cacao trees in recent years.”

Farmers can combat the mealybugs by giving vaccines to the trees to inoculate them from the virus. But the vaccines are expensive, especially for low-wage farmers, and vaccinated trees produce a smaller harvest of cacao, compounding the devastation of the virus.

Chen-Charpentier and colleagues from the University of Kansas, Prairie View A&M, the University of South Florida and the Cocoa Research Institute of Ghana have developed a new strategy: using mathematical data to determine how far apart farmers can plant vaccinated trees to prevent mealybugs from jumping from one tree to another and spreading the virus.

“Mealybugs have several ways of movement, including moving from canopy to canopy, being carried by ants or blown by the wind,” Chen-Charpentier said. “What we needed to do was create a model for cacao growers so they could know how far away they could safely plant vaccinated trees from unvaccinated trees in order to prevent the spread of the virus while keeping costs manageable for these small farmers.”

By experimenting with mathematical patterning techniques, the team created two different types of models that allow farmers to create a protective layer of vaccinated cacao trees around unvaccinated trees.

“While still experimental, these models are exciting because they would help farmers protect their crops while helping them achieve a better harvest,” Chen-Charpentier said. “This is good for the farmers’ bottom line, as well as our global addiction to chocolate.”

Engineers often speed things up using hardware that reduces the need to move so much data back and forth. While these machine-learning accelerators can streamline computation, they are susceptible to attackers who can steal secret information.

To reduce this vulnerability, researchers from MIT and the MIT-IBM Watson AI Lab created a machine-learning accelerator that is resistant to the two most common types of attacks. Their chip can keep a user’s health records, financial information, or other sensitive data private while still enabling huge AI models to run efficiently on devices.

The team developed several optimizations that enable strong security while only slightly slowing the device. Moreover, the added security does not impact the accuracy of computations. This machine-learning accelerator could be particularly beneficial for demanding AI applications like augmented and virtual reality or autonomous driving.

While implementing the chip would make a device slightly more expensive and less energy-efficient, that is sometimes a worthwhile price to pay for security, says lead author Maitreyi Ashok, an electrical engineering and computer science (EECS) graduate student at MIT.

“It is important to design with security in mind from the ground up. If you are trying to add even a minimal amount of security after a system has been designed, it is prohibitively expensive. We were able to effectively balance a lot of these tradeoffs during the design phase,” says Ashok.

Her co-authors include Saurav Maji, an EECS graduate student; Xin Zhang and John Cohn of the MIT-IBM Watson AI Lab; and senior author Anantha Chandrakasan, MIT’s chief innovation and strategy officer, dean of the School of Engineering, and the Vannevar Bush Professor of EECS. The research will be presented at the IEEE Custom Integrated Circuits Conference.

Side-channel susceptibility

The researchers targeted a type of machine-learning accelerator called digital in-memory compute. A digital IMC chip performs computations inside a device’s memory, where pieces of a machine-learning model are stored after being moved over from a central server.

The entire model is too big to store on the device, but by breaking it into pieces and reusing those pieces as much as possible, IMC chips reduce the amount of data that must be moved back and forth.

But IMC chips can be susceptible to hackers. In a side-channel attack, a hacker monitors the chip’s power consumption and uses statistical techniques to reverse-engineer data as the chip computes. In a bus-probing attack, the hacker can steal bits of the model and dataset by probing the communication between the accelerator and the off-chip memory.

Digital IMC speeds computation by performing millions of operations at once, but this complexity makes it tough to prevent attacks using traditional security measures, Ashok says.

She and her collaborators took a three-pronged approach to blocking side-channel and bus-probing attacks.

First, they employed a security measure where data in the IMC are split into random pieces. For instance, a bit zero might be split into three bits that still equal zero after a logical operation. The IMC never computes with all pieces in the same operation, so a side-channel attack could never reconstruct the real information.

But for this technique to work, random bits must be added to split the data. Because digital IMC performs millions of operations at once, generating so many random bits would involve too much computing. For their chip, the researchers found a way to simplify computations, making it easier to effectively split data while eliminating the need for random bits.

Second, they prevented bus-probing attacks using a lightweight cipher that encrypts the model stored in off-chip memory. This lightweight cipher only requires simple computations. In addition, they only decrypted the pieces of the model stored on the chip when necessary.

Third, to improve security, they generated the key that decrypts the cipher directly on the chip, rather than moving it back and forth with the model. They generated this unique key from random variations in the chip that are introduced during manufacturing, using what is known as a physically unclonable function.

“Maybe one wire is going to be a little bit thicker than another. We can use these variations to get zeros and ones out of a circuit. For every chip, we can get a random key that should be consistent because these random properties shouldn’t change significantly over time,” Ashok explains.

They reused the memory cells on the chip, leveraging the imperfections in these cells to generate the key. This requires less computation than generating a key from scratch.

“As security has become a critical issue in the design of edge devices, there is a need to develop a complete system stack focusing on secure operation. This work focuses on security for machine-learning workloads and describes a digital processor that uses cross-cutting optimization. It incorporates encrypted data access between memory and processor, approaches to preventing side-channel attacks using randomization, and exploiting variability to generate unique codes. Such designs are going to be critical in future mobile devices,” says Chandrakasan.

Safety testing

To test their chip, the researchers took on the role of hackers and tried to steal secret information using side-channel and bus-probing attacks.

Even after making millions of attempts, they couldn’t reconstruct any real information or extract pieces of the model or dataset. The cipher also remained unbreakable. By contrast, it took only about 5,000 samples to steal information from an unprotected chip.

The addition of security did reduce the energy efficiency of the accelerator, and it also required a larger chip area, which would make it more expensive to fabricate.

The team is planning to explore methods that could reduce the energy consumption and size of their chip in the future, which would make it easier to implement at scale.

“As it becomes too expensive, it becomes harder to convince someone that security is critical. Future work could explore these tradeoffs. Maybe we could make it a little less secure but easier to implement and less expensive,” Ashok says.

The research is funded, in part, by the MIT-IBM Watson AI Lab, the National Science Foundation, and a Mathworks Engineering Fellowship.

The research, published April 16 as a Reviewed Preprint in eLife, is described by editors as an important study with convincing findings of potential interest to the fields of veterinary medicine, public health and epidemiology.

Loss of livestock through abortion is a major concern for the worldwide livestock industry, resulting in significant economic loss and posing a direct threat to public health through transmission of infection. The impact of livestock abortion on the world’s poorest livestock keepers is likely to be substantial — from the direct loss of high-quality food sources and reduced income from sales of milk or meat.

“Effective livestock health surveillance provides critical data for evidence-based approaches to disease control and management, but requires reliable, high-quality and timely data drawn from multiple sources,” says lead author Felix Lankester, Clinical Associate Professor at the Paul G. Allen School for Global Health, Washington State University, Washington, US, and the Global Animal Health Tanzania, Arusha, Tanzania. “Event-based surveillance can detect early events that signal emerging human health risks, and surveillance of livestock abortion events has clear potential for identifying and preventing outbreaks of emerging diseases. However, there is limited information on the current practices, effectiveness and challenges of livestock abortion surveillance, particularly in low and middle-income countries.”

To address this gap, researchers set up a pilot livestock surveillance system in northern Tanzania in 15 wards across five districts, with a mix of pastoral, agropastoral and smallholder livestock keepers. Livestock field officers (LFOs; government employees equivalent to para-veterinarians) received training on the safe investigation of livestock abortion and were requested to report any incidents of abortions, stillbirths and perinatal death. If the cases could be followed up within 72 hours of the abortion event, further investigation including blood, milk and vaginal swabs were collected from the aborting dam, alongside tissue and swab samples from the foetus and placenta. These were tested for a wide range of infectious agents and antibodies.

Between 2017 and 2019, 215 abortion cases were reported from 150 households in 13 of the 15 wards. Of these 215 cases, 70% were reported by three (20%) of the LFOs. Most abortions were investigated within two days, and none were investigated more than four days later. Placental and foetal tissues were only collected in 24% and 34% of cases, respectively, often because these tissues were not found, but vaginal and milk samples were collected in 99% and 78% of cases.

Although data was only available for a limited number of abortions, the results revealed important insights into likely patterns and causes. For example, abortions occurred more often in the dry season, and in non-indigenous cross-bred or exotic animals than in indigenous breeds. More than a fifth of dams that aborted were reported to have experienced a previous abortion, with several experiencing multiple abortion losses, which may suggest that animals suffering recurrent abortion events may have a chronic infection that would warrant their removal from breeding stock or prevent their use as a food source.

The study emphasises the potential risks of exposure to zoonotic pathogens — infectious agents that could potentially jump from livestock to humans. In cases where an infectious agent was detected, 79% were zoonotic, and in nearly a quarter of these cases, someone had assisted with the aborted delivery, likely without any personal protective equipment. Of these, 20% were female and of reproductive age, and therefore of heightened risk from certain pathogens.

“Our study has demonstrated that livestock abortion surveillance, even at a relatively small scale, can capture valuable information about livestock pathogens, including those that are zoonotic,” says senior author Sarah Cleaveland, Professor of Comparative Epidemiology at the School of Biodiversity, One Health & Veterinary Medicine, University of Glasgow, UK. “Moreover, our pilot demonstrates the utility and feasibility of livestock abortion surveillance in rural areas and highlights that engaging field officers, establishing practical and robust field sample collections and ensuring prompt reporting of cases and feedback of results are key elements of effectiveness.”