CFP Re-reminder: JSTSP special issue on privacy

The deadline for this special issue is coming up soon!

IEEE Signal Processing Society
IEEE Journal of Selected Topics in Signal Processing
Special Issue on Signal and Information Processing for Privacy

Aims and Scope: There has been a remarkable increase in the usage of communications and information technology over the past decade. Currently, in the backend and in the cloud, reside electronic repositories that contain an enormous amount of information and data associated with the world around us. These repositories include databases for data-mining, census, social networking, medical records, etc. It is easy to forecast that our society will become increasingly reliant on applications built upon these data repositories. Unfortunately, the rate of technological advancement associated with building applications that produce and use such data has significantly outpaced the development of mechanisms that ensure the privacy of such data and the systems that process it. As a society we are currently witnessing many privacy-related concerns that have resulted from these technologies—there are now grave concerns about our communications being wiretapped, about our SSL/TLS connections being compromised, about our personal data being shared with entities we have no relationship with, etc. The problems of information exchange, interaction, and access lend themselves to fundamental information processing abstractions and theoretical analysis. The tools of rate-distortion theory, distributed compression algorithms, distributed storage codes, machine learning for feature identification and suppression, and compressive sensing and sampling theory are fundamental and can be applied to precisely formulate and quantify the tradeoff between utility and privacy in a variety of domains. Thus, while rate-distortion theory and information-theoretic privacy can provide fundamental bounds on privacy leakage of distributed data systems, the information and signal processing techniques of compressive sensing, machine learning, and graphical models are the key ingredients necessary to achieve these performance limits in a variety of applications involving streaming data, distributed data storage (cloud), and interactive data applications across a number of platforms. This special issue seeks to provide a venue for ongoing research in information and signal processing for applications where privacy concerns are paramount.

Topics of Interest include (but are not limited to):

Signal processing for information-theoretic privacy
Signal processing techniques for access control with privacy guarantees in distributed storage systems
Distributed inference and estimation with privacy guarantees
Location privacy and obfuscation of mobile device positioning
Interplay of privacy and other information processing tasks
Formalized models for adversaries and threats in applications where consumer and producer privacy is a major concern
Techniques to achieve covert or stealthy communication in support of private communications
Competitive privacy and game theoretic formulations of privacy and obfuscation

Important Dates:
Manuscript submission due: October 1, 2014
First review completed: December 15, 2014
Revised manuscript due: February 1, 2015
Second review completed: March 15, 2015
Final manuscript due: May 1, 2015
Publication date: October 2015

Prospective authors should visit the JSTSP website for information on paper submission. Manuscripts should be submitted using Manuscript Central.

Wade Trappe
Rutgers University, USA

Heejo Lee
Korea University, Korea

Lalitha Sankar
Arizona State University, USA

Srdjan Capkun

Radha Poovendran
University of Washington, USA

Microsoft Research Silicon Valley to close

I’ve been in a non-blogging mode due to classes starting and being a bit overwhelmed by everything, but the news came out today that Microsoft Silicon Valley is closing. Although the article says that some researchers may find new homes at other MSR campuses, it’s not clear who is staying and who is going. As with all of the recent industrial research lab closures/downsizings, my first thoughts are to the researchers who were working there — even if one has an inkling that things are “going bad” it still must be a shock to hear that you won’t be going to the office on Monday. Here’s hoping they land on their feet (and keep pushing the ball forward on the research front) soon!

Harvard Business Review’s underhanded game

For our first-year seminar, we wanted to get the students to read some the hyperbolic articles on data science. A classic example is the Harvard Business Review’s Data Scientist: The Sexiest Job of the 21st Century. However, when we downloaded the PDF version through the library proxy, we were informed:

Harvard Business Review and Harvard Business Publishing Newsletter content on EBSCOhost is licensed for the private individual use of authorized EBSCOhost users. It is not intended for use as assigned course material in academic institutions nor as corporate learning or training materials in businesses. Academic licensees may not use this content in electronic reserves, electronic course packs, persistent linking from syllabi or by any other means of incorporating the content into course resources

Harvard Business Publishing will be pleased to grant permission to make this content available through such means. For rates and permission, contact

So it seems that for a single article we’d have to pay extra, and since “any other means of incorporating the content” is also a violation, we couldn’t tell the students that they can go to the library website and look up an article in a publication whose name sounds like “Schmarbard Fizzness Enqueue” on sexy data science.

My first thought on seeing this restriction is that it would definitely not pass the fair use test, but then the fine folks at the American Library Association say that it’s a little murky:

Is There a Fair Use Issue? Despite any stated restrictions, fair use should apply to the print journal subscriptions. With the database however, libraries have signed a license that stipulates conditions of use, so legally are bound by the license terms. What hasn’t really been fully tested is whether federal law (i.e. copyright law) preempts a license like this. While librarians may like to think it does, there is very little case law. Also, it is possible that if Harvard could prove that course packs and article permission fees are a major revenue source for them, it would be harder to declare fair use as an issue and fail the market effect factor. In other cases as in Georgia State, the publishers could not prove their permissions business was that significant which worked against them. Remember that if Harvard could prove that schools were abusing the restrictions on use, they could sue.

Part of the ALA’s advice is to use “alternate articles to the HBR 500 supplied by other vendors that do not have these restrictions.” Luckily for us, there is no absence of hype on data science, so we could avoid it.

Given Harvard’s well-publicized open access policy and general commitment to sharing scholarly materials, the educational restriction on using materials strikes me as rank hypocrisy. Of course, maybe HBR is not really a venue for scholarly articles. Regardless, I would urge anyone considering including HBR material in their class to think twice before playing their game. Or to indulge in some civil disobedience, but this might end up hurting the libraries and not HBR, so it’s hard to figure out what to do.

Ethical questions in research funding: the case of ethics centers

I read a piece in Inside Higher Ed today on the ethics of accepting funds from different sources. In engineering, this is certainly an important issue, but the article focused Cynthia Jones, an ethics professor at UT-Pan American who directs the PACE ethics center. Jones had this stunningly ignorant thing to say about Department of Defense funding:

“What the hell are we going to use lasers for except to kill people?” Jones said. “But scientists get cut the slack.”

I’m flabbergasted that someone who works on philosophy applied to a technological field, namely biomedical ethics, believes that the only use of lasers is to kill people. Perhaps she thinks that using lasers in surgery is unethical. Or, more likely, she is unaware of how basic research in science is actually funded in this country.

Certainly, there’s been a definite shift over time in how defense-related agencies have targeted their funds — they fund much less basic research (or basic applied research) and have focused more on deliverables and technologies that more directly support combat, future warriors, and the like. This presents important ethical questions for researchers who may oppose the use of military force (or how it has been used recently) but who are interested in problems that could be “spun” towards satisfying these new objectives from DARPA, ARO, ONR, and AFOSR. Likewise, there are difficult questions about the line between independent research and consulting work for companies who may fund your graduate students. Drawing sharp distinctions in these situations is hard — everybody has their own comfort zone.

Jones wrote an article on “Dirty Money” that tries to develop rules for when money is tainted and when it is not. She comes up with a checklist at the end of the article that says funds should not be accepted if they

1- are illegal or that operate illegally in one’s country, or when the funding violates a generally accepted doctrine signed by one’s country (keeping in mind there is sometimes a distinction between legally acceptable and morally acceptable); or
2- originate from a donor who adds controls that would conflict with the explicit or implicit goals of the project to be funded or that would conflict with the proper functioning of the project or the profession’s ethical guidelines.

This, she says, is “the moral minimum.” This framing (and the problem in general of funding centers) that she addresses sidesteps the ethical questions around research that is funded by writing proposals, and indeed the question of soliciting funds. Even in the world of charitable giving, the idea that funders wander through the desert with bags of money searching for fundees seems odd. I think the more difficult ethical quandary is that of solicitation. At a “moral minimum” the fundee has to think about these questions, but I think point 2 needs a lot more unpacking because of the chicken-and-egg question of matching proposed research to program goals.

I don’t want to sound so super-negative! I think it’s great that someone is looking at the ethics of the economics of how we fund research. It’s just that there’s a whole murkier lake beyond the murky pond of funding centers, and the moral issues of science/engineering funding are not nearly as simple as Jones’s remark indicates.

Towards multi-sensor characterizations of pianos

As an undergraduate I became interested in how timbre can be used to identify musical instruments. This was largely due to my first UROP (undergraduate research gig) with Keith Martin at the MIT Media Lab. Keith’s thesis was on identifying musical instruments from spectral features, and I worked a bit on this under Ryan Rifkin in a later UROP. I’ve been catching up on podcasts during my commute to campus this week, and a semi-recent Science Friday piece on the Steinway factory was on deck for this morning.

The piece talks about work in Agnieszka Roginska‘s lab at NYU, and in particular work from a paper from last year on measuring radiation patterns in piano soundboards. The radiation patterns are pretty but a bit hard to interpret, largely because I’m way out of the acoustical signal processing world. However, what’s interesting to me is that we’re still largely focused on overtones/cepstral coefficients. I wonder about how one might discover more interesting features to characterize this data. (I know someone will suggest deep learning but color me a little skeptical).

As a side note, one of the recent popular articles from JASA is on the acoustics of coffee roasting.

Research Linkage

I’ve been a bit bogged down upon getting back from traveling, but here are a few interesting technical tidbits that came through.

Aaron Roth and Cynthia Dwork’s Foundation and Trends monograph on differential privacy is now available.

Speaking of differential privacy, Shiva Kasiviswanathan and Adam Smith have a paper in the Journal of Privacy and Confidentiality on Bayesian interpretations of differential privacy risk.

Deborah Mayo has a post up on whether p-values are error probabilities.

Raymond Yeung is offering a Coursera course on information theory (via the IT Society).

A CS Theory take on Fano’s inequality from Suresh over at the GeomBlog.

Teaching bleg: articles on “data” suitable for first-year undergraduates

My colleague Waheed Bajwa and I are teaching a Rutgers Byrne Seminar for first-year undergraduates this fall. The title of the course is Data: What is it Good For? (Absolutely Something), a reference which I am sure will be completely lost on the undergrads. The point of the course is to talk about “data” (what is it, exactly?), how it gets turned into “information,” and then perhaps even “knowledge,” with all of the pitfalls along the way. So it’s a good opportunity to talk about philosophy (e.g. epistemology), mathematics/statistics (e.g. undersampling, bias, analysis), engineering (e.g. storage, transmission), science (e.g. reduplication, retraction), and policy (e.g. privacy). It’s supposed to be a seminar class with lots of discussion, and the students can be expected to do a little reading outside of class. We have a full roster of 20 signed up, so managing the discussion might be a bit tricky, of course.

We’re in the process of collecting reading materials — magazine articles, book chapters, blog posts, etc. for the students to read. We explicitly didn’t want it to be for “technical” students only. Do any readers of the blog have great articles suitable for first-year undergrads across all majors?

As the class progresses I will post materials here, as well as some snapshot of the discussion. It’s my first time teaching a class of this type (or indeed any undergraduates at Rutgers) so I’m excited (and perhaps a bit nervous).

On a side note, Edwin Starr’s shirt is awesome and I want one.