Wednesday, September 14, 2005
US Companies Sponsor Pro Gamers
source: http://games.slashdot.org/games/05/09/14/1342207.shtml?tid=129&tid=10
The iPod Generation: going deaf?
What's that you say?
Scientists are claiming that the increased use of headphones amongst those trend-lovin' young hipsters is damaging their hearing. Robert Novak, director of clinical education in audiology at Purdue University, has been testing random students over the years, and he says that incidence of noise-induced hearing loss is on the rise.
The cause? The ubiquity of personal music players is certainly one factor—perhaps the major factor—in hearing loss among the relatively young, but many of us also live generally "louder" lifestyles. For instance, I often use a Bluetooth headset with my cell phone, and I sometimes have it cranked up in order to drown out the sounds I'm hearing with my other ear (something I don't do while driving, for the record). But that's not the end of it: how many of us spend hours a day in front of a computer listening to MP3s or even watching movies or TV at loud volume while working? I'm certainly guilty.
So what's the best practice? Keep it quiet, yo!
Meinke says a good rule of thumb comes from a study published in December: Researchers at Boston Children’s Hospital determined that listening to a portable music player with headphones at 60 percent of its potential volume for one hour a day is relatively safe.
Part of the problem, I think, is that people just don't have a sense of what sound levels are safe to begin with. We all know that rock concerts are outstandingly loud events. That leaves a lot of grey area, however. Given recent studies that suggest that as many as 5 million Americans aged 9 to 16 have hearing loss derived from noise exposure, and 50 million Americans have early signs of noise-induced hearing loss, this is no minor matter.
Should companies like Apple take the initiative and add functionality to music players that assess the safety of output sound levels? Given the wide-variety of headphone options and quality, is it even possible? One thing seems clear: turning a deaf ear to this problem isn't wise.
source:http://arstechnica.com/news.ars/post/20050913-5305.html
Data dangers dog hard drive sales
Letters, resumes, spreadsheets, phone numbers and e-mail addresses were all found on storage hardware bought and analysed by forensics firm Disklabs.
Also recoverable were temporary files from net browsers which contained login details and passwords for websites and even online bank accounts.
The problems arose because sellers were only taking basic steps to delete data.
Key change
In its test of how good users were at destroying data, Disklabs bought 100 hard drives and 50 memory cards - which included SD cards, flash drives, sim cards and memory sticks - from the auction site.
| | With not a massive amount of work we could go in there and help ourselves to whatever we want Simon Steggles, Disklabs |
"Most people made only cursory attempts to erase the data," said Mr Steggles, "and some had not done even that."
During its investigation, Disklabs found large amounts of personal and confidential business data on storage hardware.
Most worryingly, said Mr Steggles, it was possible to extract the temporary files that Microsoft's Internet Explorer browser uses to keep track of what people do when they are using the web.
With a little work, it was possible to reconstruct almost everything that some users did online, and to grab cookies and login details for sites they visited.
"With not a massive amount of work we could go in there and help ourselves to whatever we want," he told the BBC website.
In many cases, only the delete key was used to remove data. However, in PCs and many other digital devices all this does is apply a label that says these sections of storage can be over-written.
On large disk drives this can mean the supposedly deleted data remains intact for a long time.
In such cases, said Mr Steggles, recovering data is very straight-forward for forensic firms and, perhaps, technically-aware thieves.
What users needed to realise, he said, was how hard it was to destroy data. Even formatting hard drives and other memory cards would not irrevocably remove information stored on them.
If users were worried about potentially sensitive data, said Mr Steggles, they should use a professional forensics firm to erase it
"Alternatively," he said "they could smash it to bits."
Acoustic Snooping on Typed Information
Li Zhuang, Feng Zhou, and Doug Tygar have an interesting new paper showing that if you have an audio recording of somebody typing on an ordinary computer keyboard for fifteen minutes or so, you can figure out everything they typed. The idea is that different keys tend to make slightly different sounds, and although you don’t know in advance which keys make which sounds, you can use machine learning to figure that out, assuming that the person is mostly typing English text. (Presumably it would work for other languages too.)
Asonov and Agrawal had a similar result previously, but they had to assume (unrealistically) that you started out with a recording of the person typing a known training text on the target keyboard. The new method eliminates that requirement, and so appears to be viable in practice.
The algorithm works in three basic stages. First, it isolates the sound of each individual keystroke. Second, it takes all of the recorded keystrokes and puts them into about fifty categories, where the keystrokes within each category sound very similar. Third, it uses fancy machine learning methods to recover the sequence of characters typed, under the assumption that the sequence has the statistical characteristics of English text.
The third stage is the hardest one. You start out with the keystrokes put into categories, so that the sequence of keystrokes has been reduced a sequence of category-identifiers — something like this:
35, 12, 8, 14, 17, 35, 6, 44, …
(This means that the first keystroke is in category 35, the second is in category 12, and so on. Remember that keystrokes in the same category sound alike.) At this point you assume that each key on the keyboard usually (but not always) generates a particular category, but you don’t know which key generates which category. Sometimes two keys will tend to generate the same category, so that you can’t tell them apart except by context. And some keystrokes generate a category that doesn’t seem to match the character in the original text, because the key happened to sound different that time, or because the categorization algorithm isn’t perfect, or because the typist made a mistake and typed a garbbge charaacter.
The only advantage you have is that English text has persistent regularities. For example, the two-letter sequence “th” is much more common that “rq”, and the word “the” is much more common than “xprld”. This turns out to be enough for modern machine learning methods to do the job, despite the difficulties I described in the previous paragraph. The recovered text gets about 95% of the characters right, and about 90% of the words. It’s quite readable.
[Exercise for geeky readers: Assume that there is a one-to-one mapping between characters and categories, and that each character in the (unknown) input text is translated infallibly into the corresponding category. Assume also that the input is typical English text. Given the output category-sequence, how would you recover the input text? About how long would the input have to be to make this feasible?]
If the user typed a password, that can be recovered too. Although passwords don’t have the same statistical properties as ordinary text (unless they’re chosen badly), this doesn’t pose a problem as long as the password-typing is accompanied by enough English-typing. The algorithm doesn’t always recover the exact password, but it can come up with a short list of possible passwords, and the real password is almost always on this list.
This is yet another reminder of how much computer security depends on controlling physical access to the computer. We’ve always known that anybody who can open up a computer and work on it with tools can control what it does. Results like this new one show that getting close to a machine with sensors (such as microphones, cameras, power monitors) may compromise the machine’s secrecy.
There are even some preliminary results showing that computers make slightly different noises depending on what computations they are doing, and that it might be possible to recover encryption keys if you have an audio recording of the computer doing decryption operations.
I think I’ll go shut my office door now.
source: http://www.freedom-to-tinker.com/?p=893
