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Here’s how machine learning can violate your privacy

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theconversation.com – Jordan Awan, Assistant Professor of Statistics, Purdue – 2024-05-23 07:29:26

If your data was used to train an AI, it might – or might not – be safe from prying eyes.

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Jordan Awan, Purdue University

Machine learning has pushed the boundaries in several fields, personalized medicine, self-driving cars and customized advertisements. Research has shown, however, that these memorize aspects of the data they were trained with in order to learn patterns, which raises concerns for privacy.

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In statistics and machine learning, the goal is to learn from past data to make new predictions or inferences about future data. In order to achieve this goal, the statistician or machine learning expert selects a model to capture the suspected patterns in the data. A model applies a simplifying structure to the data, which makes it possible to learn patterns and make predictions.

Complex machine learning models have some inherent pros and cons. On the positive side, they can learn much more complex patterns and work with richer datasets for tasks such as image recognition and predicting how a specific person will respond to a treatment.

However, they also have the risk of overfitting to the data. This means that they make accurate predictions about the data they were trained with but start to learn additional aspects of the data that are not directly related to the task at hand. This to models that aren't generalized, meaning they perform poorly on new data that is the same type but not exactly the same as the data.

While there are techniques to address the predictive error associated with overfitting, there are also privacy concerns from being able to learn so much from the data.

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How machine learning algorithms make inferences

Each model has a certain number of parameters. A parameter is an element of a model that can be changed. Each parameter has a value, or setting, that the model derives from the training data. Parameters can be thought of as the different knobs that can be turned to affect the performance of the algorithm. While a straight-line pattern has only two knobs, the slope and intercept, machine learning models have a great many parameters. For example, the language model GPT-3, has 175 .

In order to choose the parameters, machine learning methods use training data with the goal of minimizing the predictive error on the training data. For example, if the goal is to predict whether a person would respond well to a certain medical treatment based on their medical history, the machine learning model would make predictions about the data where the model's developers know whether someone responded well or poorly. The model is rewarded for predictions that are correct and penalized for incorrect predictions, which leads the algorithm to adjust its parameters – that is, turn some of the “knobs” – and try again.

The basics of machine learning explained.

To avoid overfitting the training data, machine learning models are checked against a validation dataset as well. The validation dataset is a separate dataset that is not used in the training . By checking the machine learning model's performance on this validation dataset, developers can ensure that the model is able to generalize its learning beyond the training data, avoiding overfitting.

While this process succeeds at ensuring good performance of the machine learning model, it does not directly prevent the machine learning model from memorizing information in the training data.

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Privacy concerns

Because of the large number of parameters in machine learning models, there is a potential that the machine learning method memorizes some data it was trained on. In fact, this is a widespread phenomenon, and users can extract the memorized data from the machine learning model by using queries tailored to get the data.

If the training data contains sensitive information, such as medical or genomic data, then the privacy of the people whose data was used to train the model could be compromised. Recent research showed that it is actually necessary for machine learning models to memorize aspects of the training data in order to get optimal performance solving certain problems. This indicates that there may be a fundamental trade-off between the performance of a machine learning method and privacy.

Machine learning models also make it possible to predict sensitive information using seemingly nonsensitive data. For example, Target was able to predict which customers were likely pregnant by analyzing habits of customers who registered with the Target baby registry. Once the model was trained on this dataset, it was able to send pregnancy-related advertisements to customers it suspected were pregnant because they purchased items such as supplements or unscented lotions.

Is privacy protection even possible?

While there have been many proposed methods to reduce memorization in machine learning methods, most have been largely ineffective. Currently, the most promising solution to this problem is to ensure a mathematical limit on the privacy risk.

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The -of-the-art method for formal privacy protection is differential privacy. Differential privacy requires that a machine learning model does not change much if one individual's data is changed in the training dataset. Differential privacy methods achieve this guarantee by introducing additional randomness into the algorithm learning that “covers up” the contribution of any particular individual. Once a method is protected with differential privacy, no possible attack can violate that privacy guarantee.

Even if a machine learning model is trained using differential privacy, however, that does not prevent it from making sensitive inferences such as in the Target example. To prevent these privacy violations, all data transmitted to the organization needs to be protected. This approach is called local differential privacy, and Apple and Google have implemented it.

Differential privacy is a method for protecting people's privacy when their data is included in large datasets.

Because differential privacy limits how much the machine learning model can depend on one individual's data, this prevents memorization. Unfortunately, it also limits the performance of the machine learning methods. Because of this trade-off, there are critiques on the usefulness of differential privacy, since it often results in a significant drop in performance.

Going forward

Due to the tension between inferential learning and privacy concerns, there is ultimately a societal question of which is more important in which contexts. When data does not contain sensitive information, it is easy to recommend using the most powerful machine learning methods available.

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When working with sensitive data, however, it is important to weigh the consequences of privacy leaks, and it may be necessary to sacrifice some machine learning performance in order to protect the privacy of the people whose data trained the model.The Conversation

Jordan Awan, Assistant Professor of Statistics, Purdue University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

The Conversation

Vaccines tell a success story that Robert F. Kennedy Jr. and Trump forget – here are some key reminders

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theconversation.com – Mark R. O'Brian, Professor and Chair of Biochemistry, Jacobs School of Medicine and Biomedical Sciences, at Buffalo – 2024-07-26 07:11:29
Many fatal childhood illnesses can be prevented with vaccination.
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Mark R. O'Brian, University at Buffalo

Vaccinations have provided significant protection for the public against infectious diseases. However, there was a modest decrease in support in 2023 nationwide for vaccine requirements for to attend public schools.

In addition, the presidential candidacy of Robert F. Kennedy Jr., a leading critic of childhood vaccination, has given him a prominent platform in which to amplify his views. This includes an extensive interview on the “Joe Rogan Experience,” a podcast with over 14 million subscribers. Notably, former President Donald Trump has said he is opposed to mandatory school COVID-19 vaccinations, and in a phone call Trump apparently wasn't aware was being recorded, he appeared to endorse Kennedy's views toward vaccines.

I am a biochemist and molecular biologist studying the roles microbes play in and disease. I also teach medical and am interested in how the public understands science.

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Here are some facts about vaccines that skeptics like Kennedy get wrong:

Vaccines are effective and safe

Public health data from 1974 to the present conclude that vaccines have saved at least 154 million lives worldwide over the past 50 years. Vaccines are also constantly monitored for safety in the U.S.

Nevertheless, the false claim that vaccines cause autism persists despite study after study of large populations throughout the world showing no causal link between them.

Claims about the dangers of vaccines often come from misrepresenting scientific research papers. Kennedy cites a 2005 report allegedly showing massive brain inflammation in monkeys in response to vaccination, when in fact the authors of that study state that there were no serious medical complications. A separate 2003 study that Kennedy claimed showed a 1,135% increase in autism in vaccinated versus unvaccinated children actually found no consistent significant association between vaccines and neurodevelopmental outcomes.

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Kennedy also claims that a 2002 vaccine study included a control group of children 6 months of age and younger who were fed mercury-contaminated tuna sandwiches. This claim is false.

Gloved hands of clinician placing bandaid on child's arm, a syringe and vaccine vial beside them
Vaccines are continuously monitored for safety before and long after they're available to the general public.
Elena Zaretskaya/Moment via Getty Images

Aluminum adjuvants help boost immunity

Kennedy is co-counsel with a law firm that is suing the pharmaceutical company Merck based in part on the unfounded assertion that the aluminum in one of its vaccines causes neurological disease. Aluminum is added to many vaccines as an adjuvant to strengthen the body's immune response to the vaccine, thereby enhancing the body's defense against the targeted microbe.

The law firm's claim is based on a 2020 report showing that brain tissue from some patients with Alzheimer's disease, autism and multiple sclerosis have elevated levels of aluminum. The authors of that study do not assert that vaccines are the source of the aluminum, and vaccines are unlikely to be the culprit.

Notably, the brain samples analyzed in that study were from 47- to 105-year-old patients. Most people are exposed to aluminum primarily through their diets, and aluminum is eliminated from the body within days. Therefore, aluminum exposure from childhood vaccines is not expected to persist in those patients.

Vaccines undergo the same approval process as other drugs

Clinical trials for vaccines and other drugs are blinded, randomized and placebo-controlled studies. For a vaccine trial, this means that participants are randomly divided into one group that receives the vaccine and a second group that receives a placebo saline solution. The researchers carrying out the study, and sometimes the participants, do not know who has received the vaccine or the placebo until the study has finished. This eliminates bias.

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Results are published in the public domain. For example, vaccine trial data for COVID-19, human papilloma virus and rotavirus is available for anyone to access.

Vaccine manufacturers are liable for injury or death

Kennedy's against Merck contradicts his insistence that vaccine manufacturers are fully immune from litigation.

His claim is based on an incorrect interpretation of the National Vaccine Injury Compensation Program, or VICP. VICP is a no-fault federal program created to reduce frivolous lawsuits against vaccine manufacturers, which threaten to cause vaccine shortages and a resurgence of vaccine-preventable disease.

A person claiming injury from a vaccine can petition the U.S. Court of Federal Claims through the VICP for monetary compensation. If the VICP petition is denied, the claimant can then sue the vaccine manufacturer.

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Gloved hand picking up vaccine vial among a tray of vaccine vials
Drug manufacturers are liable for any vaccine-related or injury.
Andreas Ren Photography Germany/Image Source via Getty Images

The majority of cases resolved under the VICP end in a negotiated settlement between parties without establishing that a vaccine was the cause of the claimed injury. Kennedy and his law firm have incorrectly used the payouts under the VICP to assert that vaccines are unsafe.

The VICP gets the vaccine manufacturer off the hook only if it has complied with all requirements of the Federal Food, Drug and Cosmetic Act and exercised due care. It does not protect the vaccine maker from claims of fraud or withholding information regarding the safety or efficacy of the vaccine during its or after approval.

Good nutrition and sanitation are not substitutes for vaccination

Kennedy asserts that populations with adequate nutrition do not need vaccines to avoid infectious diseases. While it is clear that improvements in nutrition, sanitation, treatment, food safety and public health measures have played important roles in reducing deaths and severe complications from infectious diseases, these factors do not eliminate the need for vaccines.

After World War II, the U.S. was a wealthy nation with substantial health-related . Yet, Americans reported an average of 1 million cases per year of now-preventable infectious diseases.

Vaccines introduced or expanded in the 1950s and 1960s against diseases like diphtheria, pertussis, tetanus, measles, polio, mumps, rubella and Haemophilus influenza type B have resulted in the near or complete eradication of those diseases.

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It's easy to forget why many infectious diseases are rarely encountered today. The success of vaccines does not always tell its own story. It must be retold again and again to counter misinformation.The Conversation

Mark R. O'Brian, Professor and Chair of Biochemistry, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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Tagging seals with sensors helps scientists track ocean currents and a changing climate

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theconversation.com – Lilian (Lily) Dove, Postdoctoral Fellow of Oceanography, Brown – 2024-07-25 07:08:14

Tagging seals with sensors helps scientists track ocean currents and a changing climate

Lilian Dove, Brown University

A surprising technique has helped scientists observe how Earth's oceans are changing, and it's not using specialized robots or artificial intelligence. It's tagging seals.

Several species of seals around and on Antarctica and regularly dive more than 100 meters in search of their next meal. These seals are experts at swimming through the vigorous ocean currents that make up the Southern Ocean. Their tolerance for deep waters and ability to navigate rough currents make these adventurous creatures the perfect research assistants to oceanographers like my colleagues and me study the Southern Ocean.

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Seal sensors

Researchers have been attaching tags to the foreheads of seals for the past two decades to collect data in remote and inaccessible regions. A researcher tags the seal during mating season, when the marine mammal to shore to rest, and the tag remains attached to the seal for a year.

A researcher glues the tag to the seal's head – tagging seals does not affect their behavior. The tag detaches after the seal molts and sheds its fur for a new coat each year.

The tag collects data while the seal dives and transmits its location and the scientific data back to researchers via satellite when the seal surfaces for .

First proposed in 2003, seal tagging has grown into an international collaboration with rigorous sensor accuracy standards and broad data sharing. Advances in satellite technology now allow scientists to have near-instant access to the data collected by a seal.

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New scientific discoveries aided by seals

The tags attached to seals typically carry pressure, temperature and salinity sensors, all properties used to assess the ocean's rising temperatures and changing currents. The sensors also often contain chlorophyll fluorometers, which can provide data about the 's phytoplankton concentration.

Phytoplankton are tiny organisms that form the base of the oceanic food web. Their presence often means that animals such as fish and seals are around.

The seal sensors can also tell researchers about the effects of climate change around Antarctica. Approximately 150 tons of ice melts from Antarctica every year, contributing to global sea-level rise. This melting is driven by warm water carried to the ice shelves by oceanic currents.

With the data collected by seals, oceanographers have described some of the physical pathways this warm water travels to reach ice shelves and how currents transport the resulting melted ice away from glaciers.

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Seals regularly dive under sea ice and near glacier ice shelves. These regions are challenging, and can even be dangerous, to sample with traditional oceanographic methods.

Across the open Southern Ocean, away from the Antarctic coast, seal data has also shed light on another pathway causing ocean warming. Excess heat from the atmosphere moves from the ocean surface, which is in contact with the atmosphere, down to the interior ocean in highly localized regions. In these , heat moves into the deep ocean, where it can't be dissipated out through the atmosphere.

The ocean stores most of the heat energy put into the atmosphere from human activity. So, understanding how this heat moves around helps researchers monitor oceans around the globe.

Seal behavior shaped by ocean physics

The seal data also provides marine biologists with information about the seals themselves. Scientists can determine where seals look for food. Some regions, called fronts, are hot spots for elephant seals to hunt for food.

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In fronts, the ocean's circulation creates turbulence and mixes water in a way that brings nutrients up to the ocean's surface, where phytoplankton can use them. As a result, fronts can have phytoplankton blooms, which attract fish and seals.

Scientists use the tag data to see how seals are adapting to a changing climate and warming ocean. In the short term, seals may benefit from more ice melt around the Antarctic continent, as they tend to find more food in coastal areas with holes in the ice. Rising subsurface ocean temperatures, however, may change where their prey is and ultimately threaten seals' ability to thrive.

Seals have helped scientists understand and observe some of the most remote regions on Earth. On a changing planet, seal tag data will continue to provide observations of their ocean , which has vital implications for the rest of Earth's climate system.The Conversation

Lilian Dove, Postdoctoral Fellow of Oceanography, Brown University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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Cheesemaking is a complex science – a food chemist explains the process from milk to mozzarella

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theconversation.com – John A. Lucey, Professor of Food Science, of Wisconsin- – 2024-07-24 07:18:57
Storing cheese wheels to let them age intensifies the flavor.
AP Photo/Antonio Calanni

John A. Lucey, University of Wisconsin-Madison

Cheese is a relatively simple food. It's made with milk, enzymes – these are proteins that can chop up other proteins – bacterial cultures and salt. Lots of complex chemistry goes into the cheesemaking process, which can determine whether the cheese turns out soft and gooey like mozzarella or hard and fragrant like Parmesan.

In fact, humans have been making cheese for about 10,000 years. Roman soldiers were given cheese as part of their rations. It is a nutritious food that provides protein, calcium and other minerals. Its long shelf allows it to be transported, traded and shipped long distances.

I am a food scientist at the University of Wisconsin who has studied cheese chemistry for the past 35 years.

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In the U.S., cheese is predominantly made with cow's milk. But you can also find cheese made with milk from other animals like sheep, goats and even buffalo and yak.

Unlike with yogurt, another fermented dairy product, cheesemakers whey – which is water – to make cheese. Milk is about 90% water, whereas a cheese like cheddar is less than about 38% water.

Removing water from milk to make cheese results in a harder, firmer product with a longer shelf life, since milk is very perishable and spoils quickly. Before the invention of refrigeration, milk would quickly sour. Making cheese was a way to preserve the nutrients in milk so you could eat it weeks or months in the future.

How is cheese made?

All cheesemakers first pump milk into a cheese vat and add a special enzyme called rennet. This enzyme destabilizes the proteins in the milk – the proteins then aggregate together and make a gel. The cheesemaker is essentially turning milk from a liquid into a gel.

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After anywhere from 10 minutes to an hour, depending on the type of cheese, the cheesemaker cuts this gel, typically into cubes. Cutting the gel helps some of the whey, or water, separate from the cheese curd, which is made of aggregated milk and looks like a yogurt gel. Cutting the gel into cubes lets some water escape from the newly cut surfaces through small pores, or openings, in the gel.

The cheesemaker's goal is to remove as much whey and moisture from the curd as they need to for their specific recipe. To do so, the cheesemaker might stir or heat up the curd, which helps release whey and moisture. Depending on the type of cheese made, the cheesemaker will drain the whey and water from the vat, leaving behind the cheese curds.

A man in a white lab coat, hairnet and gloves pulls a device through a large tub of white liquid.
Wisconsin Master Cheesemaker Gary Grossen cuts a vat of cheese with a cheese harp during a cheesemaking short course at the Center for Dairy Research in Madison, Wis. Cutting helps release whey during the cheesemaking .
UW Center for Dairy Research

For a harder cheese like cheddar, the cheesemaker adds salt directly to the curds while they're still in the vat. Salting the curds expels more whey and moisture. The cheesemaker then packs the curds together in forms or hoops – these are containers that shape the curds into a block or wheel and hold them there – and places them under pressure. The pressure squeezes the curds in these hoops, and they knit together to form a solid block of cheese.

Cheesemakers salt other cheeses, like mozzarella, by placing them in a salt solution called a brine. The cheese block or wheel floats in a brine tank for hours, days or even weeks. During that time, the cheese absorbs some of the salt, which adds flavor and protects against unwanted bacterial or pathogen growth.

A graphic showing the many steps between a farmer harvesting milk from cows and the cheese reaching the consumer.
The cheese production process.
UW Center for Dairy Research

Cheese is a living, fermented food

While the cheesemaker is completing all these steps, several important bacterial processes are occurring. The cheesemaker adds cheese cultures, which are bacteria they choose that produce specific flavors, at the beginning of the process. Adding them to the milk while it is still liquid gives the bacteria time to ferment the lactose in the milk.

Historically, cheesemakers used raw milk, and the bacteria in the raw milk soured the cheese. Now, cheesemakers use pasteurization, a mild heat treatment that destroys any pathogens present in the raw milk. But using this treatment means the cheesemakers need to add back in some bacteria called starters – these “start” the fermentation process.

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Pasteurization provides a more controlled process for the cheesemaker, as they can select specific bacteria to add, rather than whatever is present in the raw milk.
Essentially, these bacteria eat (ferment) the sugar – the lactose – and in doing so produce lactic acid, as well as other desirable flavor compounds in the cheese like diacetyl, which smells like hot buttered popcorn.

In some types of cheese, these cultures stay active in the cheese long after it leaves the cheese vat. Many cheesemakers age their cheeses for weeks, months or even years to give the fermentation process more time to develop the desired flavors. Aged cheeses include Parmesan, aged cheddars and Gouda.

A person in a white coat holds a wheel of cheese.
A Wisconsin cheesemaker inspects a wheel of Parmesan in the aging room. Aging is an important step in the production of many cheeses, as it allows for flavor .
The Dairy Farmers of Wisconsin

In essence, cheesemaking is a milk concentration process. Cheesemakers want their final product to have the milk proteins, fat and nutrients, without as much of the water. For example, the main milk protein that is captured in the cheesemaking process is casein. Milk might contain about 2.5% casein content, but a finished cheese like cheddar may contain about 25% casein (protein). So cheese contains lots of nutrients protein, calcium and fat.

Infinite possibilities with cheese

There are hundreds of different varieties of cow's milk cheese made across the globe, and they all start with milk. All of these different varieties are produced by adjusting the cheesemaking process.

For some cheeses, like Limburger, the cheesemaker rubs a smear – a solution containing various types of bacteria – on the cheese's surface during the aging process. For others, like Camembert, the cheesemaker places the cheese in an environment (e.g., a cave) that encourages mold growth.

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Others like bandaged cheddar are wrapped with bandages or covered with ash. Adding a bandage or ash onto the cheese's surface helps protect it from excessive mold growth, and it reduces the amount of moisture lost to evaporation. This creates a harder cheese with stronger flavors.

A man in a white apron and hat stands in a room full of shelves stacked with cheese.
Wisconsin Master Cheesemaker Joe Widmer in his brick cheese aging room. Brick cheese is a smear-ripened cheese – it is produced by applying a salt solution to the exterior of the cheese as it ages.
Dairy Farmers of Wisconsin

Over the past 60 years, cheesemakers have figured out how to select the right bacterial cultures to make cheese with specific flavors and textures. The possibilities are endless, and there's no limit to the cheesemaker's imagination.The Conversation

John A. Lucey, Professor of Food Science, University of Wisconsin-Madison

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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