System Bits: March 29

Secure data; faster data transmission; wiping unwanted data.

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Cryptographic system for controlling app access to data
Researchers at MIT and Harvard University are hoping to change the fact that users of smartphones have no idea which data items their apps are collecting, where they’re stored, and if they’re stored securely with an application they’ve developed called Sieve.

With Sieve, a Web user would store all personal data, in encrypted form, on the cloud. Any app that wanted to use specific data items would send a request to the user and receive a secret key that decrypted only those items. If the user wanted to revoke the app’s access, Sieve would re-encrypt the data with a new key.

Electrical engineering and computer science PhD student Frank Wang, one of the system’s designers, said this is a rethinking of the Web infrastructure. “Maybe it’s better that one person manages all their data. There’s one type of security and not 10 types of security. We’re trying to present an alternative model that would be beneficial to both users and applications.”

The researchers are presenting Sieve at the USENIX Symposium on Networked Systems Design and Implementation. Wang is the first author, and he’s joined by MIT associate professors of electrical engineering and computer science Nickolai Zeldovich and Vinod Vaikuntanathan, who is MIT’s Steven and Renee Finn Career Development Professor, and by James Mickens, an associate professor of computer science at Harvard University.

Interestingly, Sieve required the researchers to develop practical versions of two cutting-edge cryptographic techniques called attribute-based encryption and key homomorphism.With attribute-based encryption, data items in a file are assigned different labels, or attributes. After encryption, secret keys can be generated that unlock only particular combinations of attributes: name and zip code but not street name, for instance, or zip code and date of birth but not name.

“This a rethinking of the Web infrastructure,” Frank Wang says. “Maybe it’s better that one person manages all their data. There’s one type of security and not 10 types of security. We’re trying to present an alternative model that would be beneficial to both users and applications.” (Source: MIT)

“This a rethinking of the Web infrastructure,” Frank Wang says. “Maybe it’s better that one person manages all their data. There’s one type of security and not 10 types of security. We’re trying to present an alternative model that would be beneficial to both users and applications.”
(Source: MIT)

The problem with attribute-based encryption — and decryption — is that it’s slow. To get around that, the researchers envision that Sieve users would lump certain types of data together under a single attribute. In addition, Sieve includes tables that track the locations at which grouped data items are stored in the cloud. Each of those tables is encrypted under a single attribute, but the data they point to are encrypted using standard — and more efficient — encryption algorithms. As a consequence, the size of the data item encrypted through attribute-based encryption — the table — is fixed, which makes decryption more efficient.

Making big data more accessible
University of Illinois engineers said they have paved a fast lane on the information superhighway by creating on-ramps for big data in the process with record-breaking speeds for fiber-optic data transmission.

Graduate researcher Michael E. Liu presented the research team’s developments in oxide-VCSEL technology, which underpins fiber-optic communications systems, at the Optical Fiber Communication Conference and Exposition recently.

Pictured are, from left, graduate students Curtis Wang and Michael Liu with professor Milton Feng. (Source: University of Illinois)

Pictured are, from left, graduate students Curtis Wang and Michael Liu with professor Milton Feng. (Source: University of Illinois)

The research team was led by electrical and computer engineering professor Milton Feng, and also included professor emeritus Nick Holonyak, Jr and graduate researcher Curtis Yilin Wang.

The team explained that as big data has gotten bigger, the need has grown for a high-speed data transmission infrastructure that can accommodate the ever-growing volume of bits transferred from one place to another. Their big question has always been, how do you make information transmit faster?

There is a lot of data out there, but if the data transmission is not fast enough, you cannot use data that’s been collected; you cannot use upcoming technologies that use large data streams, like virtual reality, they said.

Achieving high speeds at high temperatures is very difficult, due to the nature of the materials used, which prefer lower temperatures. However, computing components grow warm over extended operation, as anyone who has worked on an increasingly heated laptop can attest.

The researchers believe this type of technology is going to be used not only for data centers, but also for airborne, lightweight communications, like in airplanes, because the fiber-optic wires are much lighter than copper wire.

Wiping out unwanted data
In the advent of machine learning, Lehigh University and Columbia University researchers have created an approach to make those systems forget in order to protect data.

Yinzhi Cao, an assistant professor of computer science and engineering at Lehigh University, and Junfeng Yang of Columbia University, are working on this approach. They said considering how important this concept is to increasing security and protecting privacy, they believe that easy adoption of forgetting systems will be increasingly in demand, and the two researchers have developed a way to do it faster and more effectively than can be done using current methods.

Their concept, called “machine unlearning,” is so promising that Cao and Yang have been awarded a four-year, $1.2 million National Science Foundation grant to develop the approach.

The method developed by Cao and Yang allows learning systems to “forget,” or remove data by recomputing a small number of summations (Σ) instead of rebuilding the models that predict relationships between pieces of individual data. (Source: Lehigh University)


The method developed by Cao and Yang allows learning systems to “forget,” or remove data by recomputing a small number of summations (Σ) instead of rebuilding the models that predict relationships between pieces of individual data. (Source: Lehigh University)