Lifeboat Foundation

By studying the risk of re-identification more thoroughly, researchers were able to better articulate the fundamental requirements for information to be anonymous. They realized that a robust definition of anonymous should not rely on what side information may be available to an attacker. This led to the definition of Differential Privacy in 2006 by Cynthia Dwork, then a researcher at Microsoft. It quickly became the gold standard for privacy and has been used in global technology products like Chrome, the iPhone, and Linkedin. Even the US Census used it for the 2020 census.

Differential privacy solves the problem of side information by looking at the most powerful attacker possible: an attacker who knows everything about everyone in a population except for a single individual. Let’s call her Alice. When releasing information to such an attacker, how can you protect Alice’s privacy? If you release exact aggregate information for the whole population (e.g., the average age of the population), the attacker can compute the difference between what you shared and the expected value of the aggregate with everyone but Alice. You just revealed something personal about Alice.

The only way out is to not share the exact aggregate information but add a bit of random noise to it and only share the slightly noisy aggregate information. Even for the most well-informed of attackers, differential privacy makes it impossible to deduce what value Alice contributed. Also, note that we have talked about simple insights like aggregations and averages but the same possibilities for re-identification apply to more sophisticated insights like machine learning or AI models, and the same differential privacy techniques can be used to protect privacy by adding noise when training models. Now, we have the right tools to find the optimal tradeoff: adding more noise makes it harder for a would-be attacker to re-identify Alice’s information, but at a greater loss of data fidelity for the data analyst. Fortunately, in practice, there is a natural alignment between differential privacy and statistical significance.

Although Christmas may be several weeks behind us, various colorful LED contraptions can nowadays be found in our houses at any time of year. [Tim] got his hands on an LED curtain that came with a remote control that allows the user to set not only the color of the LEDs as a whole but also to run simple animations. But these were not your standard WS2812B strips with data lines: all the LEDs were simply connected in parallel with just two wires, so how was this even possible?

[Tim] hooked up his oscilloscope to the LED strings to find out how they worked, detailing the results in a comprehensive blog post. As it turns out, the controller briefly shorts the LED strip’s supply voltage to generate data bits, similar to the way old pulse-dialing phones worked. A tiny chip integrated into each LED picks up these pulses, but retains its internal state thanks to a capacitor that keeps the chip powered when the supply line goes low.

After reverse-engineering the protocol, [Tim] went on to implement a similar design using an ATMega328P as a controller and an ATtiny10 as the LED driver. With just a few lines of code and a 100 nF buffer capacitor across the ATtiny’s power pins, [Tim] was able to turn an LED on and off by sending pulses through the supply lines. Some work still needs to be done to fully implement a protocol as used in the LED strings, but as a proof-of-concept it shows that this kind of power-line communication is possible with standard components.

What are biomarkers? They are medical signals that can measure health in an accurate and reproducible way. Common examples include blood pressure readings, heart rate, and even genetic test results.

Modern digital devices measure several health parameters. Fitbit trackers use sensors such as accelerometers to tell how many steps we’ve taken in a day or how fast we’ve been walking. When can such novel health measures function as medical biomarkers?

Continue reading “The Future Of Medicine: Fighting Deadly Diseases With Smart Devices And Digital Biomarkers” »

https://www.youtube.com/watch?v=Y74GllySfk8

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You are on the PRO Robots channel and today we are going to talk about the main trends in science and technology for the next 10 years. How will the world of robotics change in 2022 and what will happen in the next 10 years? Experts say robots will become as commonplace in our lives as smartphones and laptops. Watch the top robotics trends in one video!

Continue reading “Main Trends of the Future | What the World Will Be Like in 10 Years | Science and Technology” »

A joint research team from the Hong Kong University of Science and Technology (HKUST) and the University of Tokyo discovered an unusual topological aspect of sodium chloride, commonly known as table salt, which will not only facilitate the understanding of the mechanism behind salt’s dissolution and formation, but may also pave the way for the future design of nanoscale conducting quantum wires.

There is a whole variety of advanced materials in our daily life, and many gadgets and technology are created through the assembly of different materials. Cellphones, for example, adopted a combination of many different substances—glass for the monitor, aluminum alloy for the frame, and metals like gold, silver and copper for their internal wiring. But nature has its own genius way of ‘cooking’ different properties into one wonder material, or what is known as ‘topological material’.

Topology, as a mathematical concept, studies what aspects of an object are robust under a smooth deformation. For instance, we can squeeze, stretch, or twist a T-shirt, but the number its openings would still be four so long as we do not tear it apart. The discovery of topological phases of matter, highlighted by the 2016 Nobel Prize in Physics, suggests that certain quantum materials are inherently a combination of electrical insulators and conductors. This could necessitate a conducting boundary even when the bulk of the material is insulating. Such materials are neither classified as a metal nor an insulator, but a natural assembly of the two.

If you download music online, you can get accompanying information embedded into the digital file that might tell you the name of the song, its genre, the featured artists on a given track, the composer, and the producer. Similarly, if you download a digital photo, you can obtain information that may include the time, date, and location at which the picture was taken. That led Mustafa Doga Dogan to wonder whether engineers could do something similar for physical objects. “That way,” he mused, “we could inform ourselves faster and more reliably while walking around in a store or museum or library.”

The idea, at first, was a bit abstract for Dogan, a 4th-year Ph.D. student in the MIT Department of Electrical Engineering and Computer Science. But his thinking solidified in the latter part of 2020 when he heard about a new smartphone model with a camera that utilizes the infrared (IR) range of the electromagnetic spectrum that the naked eye can’t perceive. IR light, moreover, has a unique ability to see through certain materials that are opaque to visible light. It occurred to Dogan that this feature, in particular, could be useful.

The concept he has since come up with—while working with colleagues at MIT’s Computer Science and Artificial Intelligence Lab (CSAIL) and a research scientist at Facebook—is called InfraredTags. In place of the standard barcodes affixed to products, which may be removed or detached or become otherwise unreadable over time, these tags are unobtrusive (due to the fact that they are invisible) and far more durable, given that they’re embedded within the interior of objects fabricated on standard 3D printers.

A neural network identified a majority of anonymous mobile phone service subscribers using details about their weekly social interactions.

Researchers from Korea’s one of the best science and technology universities, KAIST’s Department of Bio and Brain Engineering, have developed a new artificial intelligence-powered light field camera that can read 3D facial expressions.

The highly capable camera uses a technique that uses infrared light to read facial expressions. Professors Ki-Hun Jeong and Doheon Lee led the research team which developed this artificial intelligence-enabled technology.

The newly developed light-field camera comes with micro-lens arrays in front of the image sensor, allowing it to capture the spatial and directional information of light in a single shot, making it tiny enough to fit into a smartphone.