Friday, November 2, 2007
Researchers have invented a computer architecture that enables the secure transmission of crucial rescue information to first responders during events such as natural disasters, fires or terrorist attacks.
Electrical engineering professor Ruby Lee said the new architecture allows for what she describes as "transient trust" -- the ability to transmit sensitive information to parties on an as-needed basis so that it cannot be intercepted by others and so that access stops as soon as the recipient no longer has a legitimate need for it.
Data provided on a transient-trust basis might include floor plans of a building, medical information about occupants, or satellite maps of a given area.
The new SP (Secret Protection) computer architecture relies on two new elements that are embedded in the hardware of an electronic device: a "device root key" and a "storage root hash."
A trusted authority such as a municipal Fire Department would initialize a device -- for example, a PDA used by a firefighter -- with these features so that during an emergency a firefighter could be given access to relevant floor plans, security codes or other essential information. Once the emergency was over, the access to this sensitive information would end.
This new research emerged from the Princeton Architecture Lab for Multimedia and Security (PALMS) led by Lee, the Forrest G. Hamrick Professor of Engineering. The lab's major focus is "clean-slate" computer architecture design. As chief computer architect at Hewlett-Packard, Lee was a key figure in a revolution in computer architecture that swept through the industry in the 1980s. Since coming to Princeton, Lee has been working to revolutionize computer architecture again.
"Computers were not originally designed with security as a goal," said Lee. "I'm trying to rethink the design of computers so they can be trustworthy while at the same time retain all their original design goals, such as high performance, low cost and energy efficiency."
Lee aims to build fundamental security features directly into the hardware of a device, whether it is a personal computer, cell phone or PDA. Her researchers are working to build "trust anchors" into computer hardware to which different software can be tethered, to provide important security coverage.
Lee said that many researchers do not think it is possible to build security features into computer hardware without slowing the computer down or causing it to consume lots of power. However, research done by her lab demonstrates that this is not the case.
"Our research shows that these hardware 'roots of trust' are actually quite deployable on consumer devices like desktop computers or PDAs, and also in sensor networks and larger servers," said Lee. The work is part of the SecureCore multi-university research project, funded by the NSF Cybertrust program and DARPA, which aims to integrate essential security into the hardware, software and networking at the core of commodity computing and communications devices.
In addition to trust anchors for software, Lee is also researching hardware "safety nets" to defend against software vulnerabilities that remote attackers exploit to gain entry into a computer. The ultimate goal is to inoculate individual computers and electronic devices such as cell phones against threats like viruses, worms and bots so that they cannot infect, or be used to attack, other machines.
A paper describing the new architecture by Lee and her graduate student Jeffrey Dwoskin will be presented Wed., Oct. 31, at the ACM Computer and Communications Security conference in Alexandria, Va. .
Lee's students study all aspects of building more secure microprocessors and hardware. In June, at the IEEE Symposium on Computer Arithmetic, Lee and Yedidya Hilewitz, a graduate student at Princeton, published a paper which proposes a revolutionary design of a fundamental functional unit of microprocessors that greatly expands a computer's ability to perform "advanced bit manipulation operations," which are very useful for fast cryptography and cryptanalysis, as well as for many other applications .
Lee is also studying computer architecture that prevents leakage of information through covert channels and side channels. At the International Symposium on Computer Architecture in June, Zhenghong Wang, one of Lee's graduate students, presented a paper describing a hardware approach to preventing so-called "software side-channel attacks" during which attackers exploit the cache memories that are shared between computer programs to leak secret cryptographic keys .
In September, at the Cryptographic Hardware and Embedded Systems conference, Lee's researchers, Reouven Elbaz and David Champagne, presented a hardware memory integrity solution to rebuff memory replay attacks, where attackers try to trick a computer into accepting material as still legitimate even though it has already been officially deleted. .
Lee's research has been funded by DARPA, the National Science Foundation, the Department of Defense, Intel and other companies.
(24hoursnews) Astronauts are gearing up for a tricky solar wing repair at a far end of the International Space Station (ISS) tomorrow.
Crewmembers scrounged around the orbital laboratory yesterday for supplies, crafting "cuff links" with them that will button up two rips in the solar array wing. Today, mission controllers here at Johnson Space Center (JSC) sent astronauts on another scavenger hunt to find tools for repairing the power-generating blanket.
The space station now confronts two major threats to its power supply, both of which arose during the STS-120 mission. In addition to the maimed solar wing, which generates electricity but is structurally unstable, spacewalker Dan Tani discovered unusual metallic grit in solar-array-orienting gears on Oct. 28.
Since the solar array tore during its deployment on Oct. 30, however, mission managers abandoned inspecting the gears and scrambled to make the solar wing fix a top priority.
"We've had at least three or four extra teams running throughout the shifts," said Heather Rarick, ISS flight director, of the efforts to finish detailed plans for tomorrow's spacewalk. "It's just been a fantastic effort."
Astronauts took the changes in stride as well, offering up their sparse free time to outfit the space station's newest room as well as create the solar wing-saving cufflinks. Today, cremembers prepared Parazynski's 90-foot (27.4-meter) ride on an extended robotic arm to the damaged solar wing.
"We know and understand how hard you guys are working down there," spacewalker Doug Wheelock told mission controllers last night. "We're ready to execute."
Wheelock will accompany Parazynski during the fourth and now final spacewalk of the mission. A fifth spacewalk was planned for Sunday, but mission managers cancelled the operation to focus on hashing out plans to repair the 4B solar wing, which is attached to the Port 6 truss section of the space station.
NASA awoke the 10 free-floating astronauts this morning to the song "World" by Five for Fighting. "We're looking forward to another great day working with you and building the space station," Wheelock said as he awoke, dedicating the tune to hard-working crews on the ground.
Discovery and its seven-astronaut crew are slated to leave the orbital laboratory on Nov. 5 and land at Kennedy Space Center on Nov. 7, weather permitting. NASA officials said that the crew has enough supplies to stay docked for the ISS for two more days, should the need arise.
Facebook is now valued at $15 billion by many estimates. Soon the young company will have to prove it's really worth it.
Next week in New York, Facebook is expected to unveil its new advertising strategy on the heels of an expanded advertising partnership and $240 million investment from Microsoft. The company will likely talk about selling targeted ads to members, but experts say that to build its own ad serving system, Facebook must figure out how to serve the right ads to the right people in real time.
"That's a very difficult problem at large scale, with so many ads and millions of people," said Greg Linden, founder of Findory and an architect of Amazon.com's early product recommendation and personalization engine. "And the data's not well tied to purchases like at Amazon, or even like Google, where so many Web searches are about products."
The predominant question on everyone's mind is: Can Facebook build an ad system clever enough to keep pace with the passing fancies of its social-networking members? Facebook will have to get really good at processing all of the data it has collected on its reported 49 million members--demographics, personal preferences, and social histories--to predict what advertisements they might actually like and respond in their "news feed" or next to their "wall," according to industry executives.
That's no small task. In fact, it's a massive computing problem and one that very few companies apart from Google and Amazon have mastered. That's why Facebook--a company known for its young, fun culture--has been trying to hire more seasoned experts in so-called machine learning who can develop the right algorithms for a new generation of targeted advertising, people familiar with the company say.
One tech executive characterized the challenge like this: "The company that can process the most data will win." That maxim has proven true of Google in Web search and Amazon in e-commerce sales and product recommendations. Now Facebook must figure out how to take billions of data points about its members and turn that into an automatic ad machine.
A Facebook representative declined comment for the story.
There's no question Facebook is sitting on a data goldmine, with an exhaustive amount of information on people's preferences, backgrounds, and social histories--all given voluntarily by members. Facebook has profiles that include people's favorite music, television shows, books, and hobbies; their job history, education, birth date, and marital status; as well as daily activities, social networks, and interest groups. Traditional ad networks would kill for all that information in one place.
But with that data comes some interesting machine learning problems, experts say.
Machine learning is a broad term in the field of artificial intelligence. It refers to developing algorithms that can discover patterns in data and learn from them. Google, for example, has used probabilistic Bayesian models to serve results to data searches based on keywords. With advertising, it's all about matching the right person to the right ad. And on an individual level, that's a tall order.
So some technologists focus on lumping people into groups or types, tracking their typical behaviors in aggregate, and then trying to predict what they might want or do next.
Machine learning in online advertising might involve trying many different techniques on affinity groups to figure out which work best. That's because no one obvious technique is the silver bullet for social networks--no one has solved the problem of serving ads in that setting before.
Many thorny issues can arise, too, such as trusting what people post about themselves. Social networks can be noisy in terms of data, meaning that one day a member can say he broke it off with a girlfriend and change everything about his music and film tastes. On social networks, people are prone to misspellings, random statements, and exaggeration. Also, Web surfers are on Facebook to socialize, network, or be entertained, not to buy something. In that kind of social environment, ads can be ineffective or annoying.
That's why Facebook must perfect a subtle product placement or recommendation system. To do it, it will have to invent algorithmic tricks. For example, knowing a list of people's friends isn't necessarily useful unless the system could automatically remind people of birthdays, and then advertise a specific gift the friend might like based on his or her preferences.
Aggregate Knowledge in Palo Alto, Calif., which is backed by Google investor Kleiner Perkins Caufield & Byers, may also have an ad solution for social networks. Aggregate has developed algorithms to determine what are called "affinity clusters" of people and, based on the personality profiles of those people, targets ads. It does this by looking at people's habits in aggregate, rather than as individuals.
"We do a contest of algorithms in each context...and see which works best (to serve an ad) and that's a traditional machine learning technique. But this requires massive computing power," said Paul Martino, founder of Aggregate Knowledge. Martino said his company is in talks with various social networks, but does not yet have a deal with any of them.
One of the techniques in this field is known as collaborative filtering, which Amazon used when creating its product recommendation system. Amazon's system automatically analyzes your purchase history and looks for the same buying patterns among other shoppers. By sizing up the purchase histories of similar shoppers, the system can look for the products you haven't bought yet and that other similar shoppers have. Then it can suggest items you might also like.
Facebook plans to adopt similar techniques, for people who like the same music or films, according to people familiar with the company. That way, movie or music studios could "suggest" entertainment in the form of a product placement.
Another approach is to tailor ads to a person's demonstrated behaviors, or what's called behavioral targeted advertising. That means that a site might keep track of a person over time and factor in his or her demographics and preferences. A high-income woman who has recently said she's looking for a car might receive a Lexus ad, for example.
Facebook has already come up with some machine learning tricks to allow people to search on a person's nickname, even if the person hasn't divulged that information. According to one source, the company developed an algorithm that could strip words from people's "wall" (where friends post messages) and then remove from the list all the words that weren't in the dictionary. With what remained it built a comprehensive dictionary of nicknames so that people can search on a friend's profile under alternative names.
"That's an interesting machine learning problem, and they have a million things like that," said the source.
Facebook is already clustering people into groups, such as tech geeks or music lovers, and following patterns of people's behaviors to predict other kinds of behaviors, according to people familiar with the company. For example, groups who like baseball could like sushi, in a hypothetical link within data patterns that you might not expect. The ad network could then target ads for local sushi restaurants to members of that group. But the company hasn't deployed this methodology on a wide scale.
Facebook is also a tremendous barometer for public opinion. So the site could eventually track chatter about a movie like Spider-Man, for example, and sell that information to the film company or watch how a message about the movie on a wall is received. If the chatter falls off quickly, it could be time for the studio to release the movie on DVD rather than keep it in theaters.
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Extra: 15 dumbest Apple predictions of all time The big search sites have done some work in this area. Google, for example, in order to send the right ad, looks at search terms, a person's physical location (by IP address), and type of content on the site he or she visited. Yahoo targets ads similarly, but it also sells behaviorally targeted ads to marketers. For example, Yahoo might deliver an ad for baby formula to a person the company know has looked at pregnancy-health pages.
Microsoft serves ads based on audience segment, such as car shoppers, by anonymously tracking the behavior of users across its network. If an MSN user has browsed MSN Autos or searched for "Kelly Blue Book" on Live Search, the user will see relevant ads. The search and Web browsing history data is blended with data people offer to Microsoft during registration, such as age and gender, but not identifiable information.
For now, Microsoft will be serving Facebook's graphical ads, but it remains to be seen how the social network will tackle the ad issue on its own.
"The problem isn't that they can't make revenue, it's that the expectation is so high on the amount of revenue that they can make," Linden said. "Because people aren't in a purchasing frame of mind (at Facebook), it's going to be hard for them to get as much from the advertising as the hype right now."
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