Communications of the ACM

tion, interaction, and movement— including all types of other macro-level descriptions of society and people and the world—are now available to us.

As more data is collected from a growing pool of devices, has the individual lost the right to information privacy?

MICHAEL STONEBRAKER: Imagine this simple example: you show up at your doctor’s office and have an x-ray done and you want the doctor to run a query that shows who else has x-rays that look like yours, what was their diagnosis and what was the morbidity of the patients. That requires integrating essentially the country’s entire online medical databases and presumably would extend to multiple countries as well. While that is a daunting data integration challenge, because every hospital chain stores its data with different formats, different encodings for common terms, etc., the social value gained from solving it is just huge. But that also creates an incredibly difficult privacy problem, one that is not a technical issue. Because if you’re looking for an interesting medical query, you’re not looking for common events; you’re looking for rare events, and at least to my knowledge, there aren’t any technical solutions that will allow access to rare events without indirectly disclosing who the events belong to.

I view the privacy problem to be basically a legal problem. We have to have legal remedies in this area. SINCE ITS INAUGURATION in 1966, the ACM A.M. Turing Award has recognized ma- jor contributions of lasting importance to computing.

Through the years, it has become the most prestigious award in computing. To help celebrate 50 years of the ACM Turing Award and the visionaries who have received it, ACM has launched a campaign called “Panels in Print,” which takes the form of a collection of responses from Turing laureates, ACM award recipients and other ACM experts on a given topic or trend.

For our fourth and final Panel in Print, we invited 2014 ACM A.M. Turing Award recipient MICHAEL STONEBRAKER, 2013 ACM Prize recipient DAVID BLEI, 2007 ACM Prize recipient DAPHNE KOLLER, and ACM Fellow VIPIN KUMAR to discuss trends in big data.

Gartner estimates that there are currently about 4. 9 billion connected devices (cars, homes, appliances, industrial equipment, among others) generating data. This is expected to reach 25 billion by 2020. What do you see as some of the primary challenges and opportunities this wave of data will create?

VIPIN KUMAR: One of the major challenges we are going to see is that the data being gathered from these connected devices and sensors is very different from other datasets that our big data community has had to deal with.

The biggest successes we have seen
for big data are in applications such as
Internet search, e-commerce, place-
ment of online ads, language transla-
tion, image processing, autonomous
driving. These successes have been
enabled, to a great extent, by the avail-
ability of large, relatively structured da-
tasets that can be used to train a broad
range of machine learning algorithms.
But the data from multitudes of in-
terconnected devices in its raw state,
can be highly fragmented, disparate
in space and time, and very heteroge-
neous. Analyzing such data will be a big
and new technological challenge for
the machine learning and data mining
communities.

DAVID BLEI: The key idea here is that just the data from something as simple as Netflix watching habits doesn’t provide the recommendation of a new movie; it’s that data alongside all the data from everybody else that helps make recommendations.

It’s an exciting world because we are personalizing our interaction with devices through the aggregate data of everybody using their devices. Of course, this all comes with a challenge around privacy and what we give up when we make our data available or the spectrum of how much we can give up against how much personalization power we get in return.

The other opportunity is in an unprecedented way to learn about the world through these huge collections of many individuals. This is a massive dataset, and patterns of communica-