A new approach to hiding data within executable computer program files could make it almost impossible to detect hidden documents, according to a report in the International Journal of Internet Technology and Secured Transactions.

Steganography is a form of security through obscurity in which information is hidden within an unusual medium. An artist might paint a coded message into a portrait, for instance, or an author embed words in the text. A traditional paper watermark is a well-known example of steganography in action. At first glance, there would appear to be nothing unusual about the work, but a recipient aware of the presence of the hidden message would be able to extract it easily. In the computer age, steganography has become more of a science than an art.

Those intent on hiding information from prying eyes can embed data in the many different file types that are ostensibly music files (mp3), images (jpeg), video (mpeg4) or word-processing documents. Unfortunately, there is a limit to how much hidden data can be embedded in such files without it becoming apparent that something is hidden because the file size increases beyond what one might expect to see for a common music or video file, for instance. A five minute music file in mp3 format and the widespread sampling rate of 128 kilobits per second, for instance, is expected to be about 5 megabytes in size. Much bigger and suspicions would be aroused as to the true nature of the file, examination with widely available mp3-tagging software would reveal something amiss with the file's contents. The same could be said for almost all other file types.

However, one group of files that vary enormously in size and are usually rather difficult to examine in detail because they comprise of compiled computer code are executable, or exe, files. These files tend to contain lots of what might be described as "junk data" of their own as well as internal programmer notes and identifiers, redundant sections of code and infuriatingly in some senses coding "bloat." All of this adds up to large and essentially random file sizes for exe files. As such, it might be possible to embed and hide large amounts of data in encoded form in an exe file without disrupting the file's ability to be executed, or run, as a program but crucially without anyone discovering that the exe file has a dual function.

Computer scientists Rajesh Kumar Tiwari of the GLNA Institute of Technology, in Mathura and G. Sahoo of the Birla Institute of Technology, in Mesra, Ranchi, India, have developed just such an algorithm for embedding hidden data in an executable file. They provide details in the International Journal of Internet Technology and Secured Transactions. The algorithm has been built into a program with graphical user interface that would take a conventional exe file and the data to be hidden as input and merge the two producing a viable exe file with a hidden payload. The technology could be used on smart phones, tablet PCs, portable media players and any other information device on which a user might wish to hide data.

Soaring energy consumption by ever more powerful computers, data centers and mobile devices has many experts looking to reduce the energy use of these devices. Most projects so far focus on more efficient cooling systems or energy-saving power modes.

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A University of Washington project sees a role for programmers to reduce the energy appetite of the ones and zeroes in the code itself. Researchers have created a system, called EnerJ, that reduces energy consumption in simulations by up to 50 percent, and has the potential to cut energy by as much as 90 percent. They will present the

research next week in San Jose at the Programming Language Design and Implementation annual meeting.

"We all know that energy consumption is a big problem," said authorLuis Ceze, a UW assistant professor of computer science and engineering. "With our system, mobile phone users would notice either a smaller phone, or a longer battery life, or both. Computing centers would notice a lower energy bill."

The basic idea is to take advantage of processes that can survive tiny errors that happen when, say, voltage is decreased or correctness checks are relaxed. Some examples of possible applications are streaming audio and video, games and real-time image recognition for augmented-reality applications on mobile devices.

"Image recognition already needs to be tolerant of little problems, like a speck of dust on the screen," said co-author Adrian Sampson, a UW doctoral student in computer science and engineering. "If we introduce a few more dots on the image because of errors, the algorithm should still work correctly, and we can save energy."

The UW system is a general framework that creates two interlocking pieces of code. One is the precise part -- for instance, the encryption on your bank account's password. The other portion is for all the processes that could survive occasional slipups.

The software creates an impenetrable barrier between the two pieces.

"We make it impossible to leak data from the approximate part into the precise part," Sampson said. "You're completely guaranteed that can't happen."

While computers' energy use is frustrating and expensive, there is also a more fundamental issue at stake. Some experts believe we are approaching a limit on the number of transistors that can run on a single microchip. The so-called "dark silicon problem" says that as we boost computer speeds by cramming more transistors onto each chip, there may no longer be any way to supply enough power to the chip to run all the transistors.

The UW team's approach would work like a dimmer switch, letting some transistors run at a lower voltage. Approximate tasks could run on the dimmer regions of the chip.

"When I started thinking about this, it became more and more obvious that this could be applied, at least a little bit, to almost everything," Sampson said. "It seemed like I was always finding new places where it could be applied, at least in a limited way."

Researchers would use the program with a new type of hardware where some transistors have a lower voltage, the force on electrons in the circuit. This slightly increases the risk of random errors; EnerJ shuttles only approximate tasks to these transistors.

"If you can afford one error every 100,000 operations or so, you can already save a lot of energy," Ceze said.

Other ways to use hardware to save energy are lowering the refresh rate and reducing voltage of the memory chip.

Simulations of such hardware show that running EnerJ would cut energy by about 20 to 25 percent, on average, depending on the aggressiveness of the approach. For one program the energy saved was almost 50 percent. Researchers are now designing hardware to test their results in the lab.

Today's computers could also use EnerJ with a purely software-based approach. For example, the computer could round off numbers or skip some extra accuracy checks on the approximate part of the code to save energy -- researchers estimate between 30 and 50 percent savings based on software alone.

Combining the software and hardware methods they believe they could cut power use by about 90 percent.

"Our long-term goal would be 10 times improvement in battery life," Ceze said. "I don't think it is totally out of the question to have an order of magnitude reduction if we continue squeezing unnecessary accuracy."

The program is called EnerJ because it is an extension for the Java programming language. The team hopes to release the code as an open-source tool this summer.

Co-authors of the paper are UW computer science and engineering professor Dan Grossman, postdoctoral researcher Werner Dietl, graduate student Emily Fortuna and undergraduate Danushen Gnanapragasam. Also involved in the research is doctoral student Hadi Esmaeilzadeh.