Forum for Science, Industry and Business

Sponsored by:     3M 
Search our Site:

 

Theoretical computer science provides answers to data privacy problem

08.10.2015

New tools allow researchers to share and study sensitive data safely by applying 'differential privacy'

The promise of big data lies in researchers' ability to mine massive datasets for insights that can save lives, improve services and inform our understanding of the world.


Through a differentially private interface for data analysis, it is impossible for an adversary to extract information that is specific to one individual, no matter how much other information or computing power it has at its disposal.

Credit: Salil Vadhan, Harvard University

These data may be generated by surfing the web, interacting with medical devices or passing sensors. Some data may be trivial, but in many cases, data are deeply personal. They can even influence our insurance premiums or the price we pay for a product online.

When planning a study, data scientists need to balance their desire to uncover new knowledge with the privacy of the people whom the data represent.

"The science of understanding human behavior, health, and interactions is being transformed by the ability of researchers to collect, analyze, and share data about individuals on a wide scale," a team of Harvard University researchers wrote in a July 2014 paper, "Integrating Approaches to Privacy across the Research Lifecycle."

However, the paper continued, "a major challenge for realizing the full potential of such data science is ensuring the privacy of human subjects."

Initially, researchers believed that anonymizing data--erasing the names and replacing them with arbitrary identifiers--was enough to protect the identities and personal information of those who had agreed (knowingly or unknowingly) to contribute information. However, in a well-known study published in 2000, Latanya Sweeney led a team that uncovered the identities of patients, including then Massachusetts governor William Weld, by correlating anonymized data with other publicly available data.

In a more recent case, researchers Arvind Narayanan and Vitaly Shmatikov from The University of Texas at Austin partially de-anonymized a Netflix dataset containing half a million movie reviews. By cross-referencing that dataset with information in the Internet Movie Database, the researchers showed that attackers could potentially identify known users, compromising their data.

As cases of re-identification and de-anonymization emerge, researchers are exploring new, more robust approaches to privacy protection.

Salil Vadhan, a professor of computer science at Harvard University and former director of the Center of Research on Computation and Society, is among the researchers exploring an approach known as "differential privacy" that allows one to investigate data without revealing confidential information about participants. Initially introduced by Cynthia Dwork, Frank McSherry , Kobbi Nissim and Adam Smith, among others, in the mid-2000s, researchers continue to develop the concept today to apply it for real-world problems.

As the lead researcher for the National Science Foundation (NSF) supported "Privacy Tools for Sharing Research Data," Vadhan and his team at Harvard are developing a new computer system that acts as a trusted curator--and identity protector--of sensitive, valuable, data. (The Sloan Foundation and Google, Inc. are providing the project with additional support.)

The system works like this: Researchers ask the virtual curator questions based on the data--for instance, "What percentage of individuals who have Type B blood are also HIV positive?" The computer returns an answer that is approximately accurate, but that includes just enough "noise" that no matter how hard someone tries, they cannot find out anything specific to any individual in the database.

"Even if an adversary tries to target an individual in the dataset, the adversary should not be able to tell the difference between the world as it is and one where that individual's data is entirely removed from the dataset," Vadhan said. "Randomization turns out to be very powerful."

If the system is implemented simply, the level of privacy degrades with multiple queries, so one could keep asking questions until the point where identifying people in the database becomes possible. However, by judiciously increasing the amount of noise and carefully correlating it across queries, the system can maintain privacy protection, even in the face of very large number of questions.

Differential privacy has become a hot topic in recent years. A 2015 Science magazine article referred to differential privacy as one of the most promising technical solutions for protecting the data of students enrolled in Massive Open Online Courses (MOOCs). Projects including OnTheMap, used for U.S. census data, RAPPOR, a new product from Google, apply forms of differential privacy for data sharing.

Speaking at the NSF in early 2015, Vadhan explained how ideas from theoretical computer science inspired the development of differential privacy algorithms, which have are now entering the research ecosystem. Harvard's Institute for Quantitative Social Science is planning to use differential privacy techniques to enable more researchers to share, retain control of, and credit for their data contributions as part of the Dataverse Network, a project that guarantees the long-term preservation of critical datasets.

Unlocked Scientific Potential

Dataverse is the largest public general-purpose research data repository in the world. However, the scientific community could access far more datasets that are currently not publicly available, if differential privacy's promise is fulfilled, according to Gary King, Albert J. Weatherhead III University Professor at Harvard University and Director of the Institute for Quantitative Social Science.

"That's why we're so thrilled to be working on this project," King said. "The social sciences are finally getting to the point in human history where we have enough information to move from studying problems to actually solving them. As we make progress on the privacy problem, we will be able to unlock more and more of the potential of this new information."

The differential privacy tool Vadhan and his team are developing will allow the inclusion of datasets that were previously withheld because the information was too sensitive and privacy was uncertain.

"Currently, Dataverse is not equipped to handle datasets with privacy concerns associated with them," Vadhan said. "If a researcher says that a dataset has identifiable personal information, it is not made available for download."

Differential privacy doesn't work for every type of research question. Vadhan pointed to regression, machine learning, and social network analysis as areas where there are very promising theoretical results, but challenges remain to making differential privacy work well in practice.

Differential privacy also doesn't help when you're looking for identity of a specific individual: as in the case of identifying terrorists or a match for a kidney donor. But that's the point: each individual should be "hidden" even as they contribute to the greater good of any given study.

"This project could significantly enhance the state-of-the-art in privacy," said Nina Amla, a program director at NSF who oversees the award. "They take a highly interdisciplinary approach which brings together deep expertise in computer science, social science, statistics, and law."

According to Vadhan, differential privacy has rich connections with other parts of computer science theory and mathematics.

"It turned out not to just be an island in itself but to be deeply intertwined with other theoretical questions," Vadhan said. "And we're seeing interest from many communities, such as privacy law, medical informatics and social science, to see whether differential privacy can address the privacy problems they think about."

The team hopes to release a preliminary version of their tool for public exploration and feedback this fall and have published their work in the Annals of the American Academy of Political and Social Science and will present their research on differential privacy at many major conferences, including the upcoming 2015 IEEE Symposium on Foundations of Computer Science.

"Our goal in the project is to enable the wider sharing of data while protecting privacy," Vadhan said, "and to make sharing easier for a non-expert researcher without experience in computer science, law or statistics."

Media Contact

Aaron Dubrow
adubrow@nsf.gov
703-292-4489

 @NSF

http://www.nsf.gov 

Aaron Dubrow | EurekAlert!

Further reports about: Dataverse NSF computer science privacy social science

More articles from Information Technology:

nachricht Controlling robots with brainwaves and hand gestures
20.06.2018 | Massachusetts Institute of Technology, CSAIL

nachricht Innovative autonomous system for identifying schools of fish
20.06.2018 | IMDEA Networks Institute

All articles from Information Technology >>>

The most recent press releases about innovation >>>

Die letzten 5 Focus-News des innovations-reports im Überblick:

Im Focus: Temperature-controlled fiber-optic light source with liquid core

In a recent publication in the renowned journal Optica, scientists of Leibniz-Institute of Photonic Technology (Leibniz IPHT) in Jena showed that they can accurately control the optical properties of liquid-core fiber lasers and therefore their spectral band width by temperature and pressure tuning.

Already last year, the researchers provided experimental proof of a new dynamic of hybrid solitons– temporally and spectrally stationary light waves resulting...

Im Focus: Overdosing on Calcium

Nano crystals impact stem cell fate during bone formation

Scientists from the University of Freiburg and the University of Basel identified a master regulator for bone regeneration. Prasad Shastri, Professor of...

Im Focus: AchemAsia 2019 will take place in Shanghai

Moving into its fourth decade, AchemAsia is setting out for new horizons: The International Expo and Innovation Forum for Sustainable Chemical Production will take place from 21-23 May 2019 in Shanghai, China. With an updated event profile, the eleventh edition focusses on topics that are especially relevant for the Chinese process industry, putting a strong emphasis on sustainability and innovation.

Founded in 1989 as a spin-off of ACHEMA to cater to the needs of China’s then developing industry, AchemAsia has since grown into a platform where the latest...

Im Focus: First real-time test of Li-Fi utilization for the industrial Internet of Things

The BMBF-funded OWICELLS project was successfully completed with a final presentation at the BMW plant in Munich. The presentation demonstrated a Li-Fi communication with a mobile robot, while the robot carried out usual production processes (welding, moving and testing parts) in a 5x5m² production cell. The robust, optical wireless transmission is based on spatial diversity; in other words, data is sent and received simultaneously by several LEDs and several photodiodes. The system can transmit data at more than 100 Mbit/s and five milliseconds latency.

Modern production technologies in the automobile industry must become more flexible in order to fulfil individual customer requirements.

Im Focus: Sharp images with flexible fibers

An international team of scientists has discovered a new way to transfer image information through multimodal fibers with almost no distortion - even if the fiber is bent. The results of the study, to which scientist from the Leibniz-Institute of Photonic Technology Jena (Leibniz IPHT) contributed, were published on 6thJune in the highly-cited journal Physical Review Letters.

Endoscopes allow doctors to see into a patient’s body like through a keyhole. Typically, the images are transmitted via a bundle of several hundreds of optical...

All Focus news of the innovation-report >>>

Anzeige

Anzeige

VideoLinks
Industry & Economy
Event News

Munich conference on asteroid detection, tracking and defense

13.06.2018 | Event News

2nd International Baltic Earth Conference in Denmark: “The Baltic Sea region in Transition”

08.06.2018 | Event News

ISEKI_Food 2018: Conference with Holistic View of Food Production

05.06.2018 | Event News

 
Latest News

Graphene assembled film shows higher thermal conductivity than graphite film

22.06.2018 | Materials Sciences

Fast rising bedrock below West Antarctica reveals an extremely fluid Earth mantle

22.06.2018 | Earth Sciences

Zebrafish's near 360 degree UV-vision knocks stripes off Google Street View

22.06.2018 | Life Sciences

VideoLinks
Science & Research
Overview of more VideoLinks >>>