The US Army's big data cloud app suite

Army demonstrates disputed intelligence system (Army Times)

“It is globally deployed, this is not a system that is in the lab, this is a system that is supporting and has supported nine corps, 38 divisions, 138 brigades,” said Lt. Gen. Mary Legere, the the Army’s deputy chief of staff for intelligence. “It supports today our operations in Afghanistan and the greater Middle East, Africa, the Pacific, Korea and anywhere you have soldiers who are deployed.”
...
The Army’s cloud-based system — called the Distributed Common Ground System-Army — collects raw intelligence, surveillance and reconnaissance data from 600 sources, including battlefield reports, biometrics databases, unmanned aerial systems and manned reconnaissance aircraft, as well as joint, national and strategic sources. From there, analysts can connect the dots using a variety of software tools, putting actionable intelligence in the hands of battlefield commanders.

Forty apps using data from 600 sources.

Putting the mosaic together in Boston

The post's title refers to the mosaic of information that can be arranged into a picture of the events leading up to the savage acts. The other mosaic, the way things were for so many unique individuals, can never be put back together.

How This Photo of the Boston Marathon Gives the FBI a Bounty of Data (Wired)

The photo — click to enlarge — shows a lot of people, what they’re wearing and where they’re positioned within the crush of Marathon fans. It’s important to law enforcement, as it “can be of use in putting the mosaic together,” says Robert McFadden, a former Navy terrorism investigator. Crabbe’s wide-angle panoramic photo “could be one of the many critical pieces of the map of the investigation.”

The panorama photo was one of seven shots Crabbe snapped with her phone during a leisurely stroll and later handed over to investigators.

The Wired article starts with a single data point (data set, really), a photo, and follows it part-way through the process the FBI has used during its investigation of the recent bombings in Boston.

...putting the mosaic together. It's a good metaphor for how the people charged with figuring out what happened and who did it go about their work. Read the whole thing.



Also see:
What's Going on Behind the Scenes of Bombing Investigation? Forensic Scientist, Former DHS Official Shed Light on Tech and Tactics (The Blaze)

“Facial recognition technology will play a very small part,” Schiro told TheBlaze in a phone interview.

“A lot depends on the quality of the images you have to work with,” Schiro continued noting that lighting, angle and other factors could really limit the use of facial recognition in the case. Not only that but there would need to be some sort of match for it to recognize.

UPDATE:
Here's another good article about facial recognition and crime solving. I selected the two paragraphs below because they highlight both the organizational issue of interoperability and the technology issues around matching. There are other interesting insights in the rest of the piece.

Facial Recognition Tech: New Key to Crime Solving (The Fiscal Times)

However, it's likely the FBI was unsuccessful in identifying the suspects using FR because either they didn't have a quality image of the wanted persons, or the suspects were not in any of the databases the FBI has access too, Albers said.
...
While facial recognition technology has high-accuracy when used to match a clear image of a person with another passport-style photo, it is not as effective when used with low-quality images like the ones the FBI released on Thursday. The standard for facial recognition to be accurate requires 90 pixels of resolution between the two eyes of the pictured person. The pictures the FBI released of the suspects were about 12 pixels between the two eyes, said Jim Wayman, the director of the National Biometric Center.

and..
Facial-recognition technology to help track down criminals – Humans are still better at it (Kuwait Times)

Search for Boston bombers likely relied on eyes, not software (Reuters)

These last two reminded me of the (Facial Recognition vs Human) & (Facial Recognition + Human) post from November 2011.

In the Boston case, it looks like there were two barriers to effective use of facial recognition technology in identifying the suspects. On the "evidence" (probe) side, the image quality was poor. On the enrollment (database) side the only "correct" match was likely to be in a very large database such as the Massachusetts DMV database.

If only one of these conditions were true — for example a bad probe against a small database, or good probe against a large database — facial recognition technology might have been of more help.

Crowd-sourcing the ID challenge to a large number of human beings that operate with a lot more intelligence and information than facial recognition algorithms is another option. It's been used with photographs since at least 1865 and without photographs since at least 1696.

One crowd-sourcing fact that law enforcement officials must consider, however, is that the suspect is almost certainly in the sourced crowd. If the suspect already knows he's a suspect, that's not a problem. If he doesn't already know he's suspected, that information is the price of getting the public's help which means facial recognition technology will retain its place in the criminal ID toolkit.


UPDATE:
Boston police chief: facial recognition tech didn’t help find bombing suspects (Ars Technica)

“The technology came up empty even though both Tsarnaevs’ images exist in official databases: Dzhokhar had a Massachusetts driver’s license; the brothers had legally immigrated; and Tamerlan had been the subject of some FBI investigation,” the Post reported on Saturday.

Facial recognition systems can have limited utility when a grainy, low-resolution image captured at a distance from a cellphone camera or surveillance video is compared with a known, high-quality image. Meanwhile, the FBI is expected to release a large-scale facial recognition apparatus “next year for members of the Western Identification Network, a consortium of police agencies in California and eight other Western states,” according to the San Jose Mercury News.

Securing Corporate Data in the Cloud: Biometric Logical and Physical Access Control

How to make information stored in the cloud robustly safe (The Guardian)

Most articles like this mention biometrics only in the context of physical access control at the data center, which is important. You don't hear as much about biometrics for controlling access to data stored in the cloud, though, and that's too bad because biometrics for logical access control could make the cloud a lot safer for businesses and their customers.

At a data center, physical access control prevents this (access to places)...

Image: Copyright Paramount

Logical access control is for this (access to information)...

...and tricking out data centers like Fort Knox, along with other logistical challenges ensures that there are a lot more of the latter than the former.

In networked logical access control solutions, the biometric sensor hardware is a part of the security. In the standard Username/Password regime, the hardware used, the keyboard, offers no additional security. With username/password authentication, a hacker needs only a keyboard to fill in the proper fields and she gains access to the network. If that username/password is a superuser or administrator credential, there's may be some turnover in the CTO function.

Biometric authentication is very different animal because with biometrics, the hardware layer does provide extra security. If the hacker steals a biometric or unencrypted biometric template (a long character string), she can't just type it in even if she finds the place in the programming that handles the template. The template resulting from a verification attempt is like a single use password created during the interaction of a physical object (body part) with certain known sensor.

For organizations using cloud services, requiring biometric authentication to authorize access to any database storing usernames/passwords should dramatically decrease the risk of high profile data breaches or other damaging hacks.

But even if every human account used biometrics for logical access control, some version of username/password verification will be around for a long, long time because username/password is a cheap, well-understood, flexible technology that supports certain access control models that biometrics does not. For one thing, people aren't the only things that claim an identity before accessing IT systems — computers do it, too, and they don't have biometrics.

The challenge that system designers now face is to identify where using Username/Password is too dangerous, and where biometrics can be used to reduce risk to an acceptable level. This requires identifying everything currently authenticated with a username/password and a determining which of these things are more efficiently protected using biometric authentication, then implementing the change. This is far easier said than done.

Requiring biometric verification of all human Administrator logins would be a good start, though. There aren't (or shouldn't be) that many of them. They are tech savvy, so they should be able to adapt to the new security environment quickly. They should have an understanding of why the extra step is worth the effort. It's their responsibility to keep the keys of the kingdom and they're the ones on the hot seat when the CEO is out apologizing to all and sundry following a data breach.

Big Data's Impact in the World

Privacy advocates tend to latch onto biometrics as a convenient way of expressing concerns about a world driven by Big Data even though biometrics will only ever be a tiny slice of the data pie.

Big data does, however, present opportunities and challenges that are well worth considering. The opportunities are so great that big data techniques will, and probably should be, adopted. The challenges to individual privacy are real, too.

This article provides a great overview of what is becoming possible and where we may be headed.

The Age of Big Data (New York Times)

Data is not only becoming more available but also more understandable to computers. Most of the Big Data surge is data in the wild — unruly stuff like words, images and video on the Web and those streams of sensor data. It is called unstructured data and is not typically grist for traditional databases.

But the computer tools for gleaning knowledge and insights from the Internet era’s vast trove of unstructured data are fast gaining ground. At the forefront are the rapidly advancing techniques of artificial intelligence like natural-language processing, pattern recognition and machine learning.

Those artificial-intelligence technologies can be applied in many fields. For example, Google’s search and ad business and its experimental robot cars, which have navigated thousands of miles of California roads, both use a bundle of artificial-intelligence tricks. Both are daunting Big Data challenges, parsing vast quantities of data and making decisions instantaneously.

The wealth of new data, in turn, accelerates advances in computing — a virtuous circle of Big Data. Machine-learning algorithms, for example, learn on data, and the more data, the more the machines learn. Take Siri, the talking, question-answering application in iPhones, which Apple introduced last fall. Its origins go back to a Pentagon research project that was then spun off as a Silicon Valley start-up. Apple bought Siri in 2010, and kept feeding it more data. Now, with people supplying millions of questions, Siri is becoming an increasingly adept personal assistant, offering reminders, weather reports, restaurant suggestions and answers to an expanding universe of questions.

That's just a sample. It's well worth reading the whole thing.