Entries Tagged "academic papers"

Page 44 of 88

Identifying People from Their Metadata

Jonathan Mayer, Patrick Mutchler, and John C. Mitchell, “Evaluating the privacy properties of telephone metadata“:

Abstract: Since 2013, a stream of disclosures has prompted reconsideration of surveillance law and policy. One of the most controversial principles, both in the United States and abroad, is that communications metadata receives substantially less protection than communications content. Several nations currently collect telephone metadata in bulk, including on their own citizens. In this paper, we attempt to shed light on the privacy properties of telephone metadata. Using a crowdsourcing methodology, we demonstrate that telephone metadata is densely interconnected, can trivially be reidentified, and can be used to draw sensitive inferences.

New research, but not a new result. There have been several similar studies over the years. This one uses only anonymized call and SMS metadata to identify people who volunteered for the study.

Four assorted news articles.

Posted on May 19, 2016 at 6:10 AM • View Comments

Hacking Gesture-Based Security

Interesting research: Abdul Serwadda, Vir V. Phoha, Zibo Wang, Rajesh Kumar, and Diksha Shukla, “Robotic Robbery on the Touch Screen,” ACM Transactions on Information and System Security, May 2016.

Abstract: Despite the tremendous amount of research fronting the use of touch gestures as a mechanism of continuous authentication on smart phones, very little research has been conducted to evaluate how these systems could behave if attacked by sophisticated adversaries. In this article, we present two Lego-driven robotic attacks on touch-based authentication: a population statistics-driven attack and a user-tailored attack. The population statistics-driven attack is based on patterns gleaned from a large population of users, whereas the user-tailored attack is launched based on samples stolen from the victim. Both attacks are launched by a Lego robot that is trained on how to swipe on the touch screen. Using seven verification algorithms and a large dataset of users, we show that the attacks cause the system’s mean false acceptance rate (FAR) to increase by up to fivefold relative to the mean FAR seen under the standard zero-effort impostor attack. The article demonstrates the threat that robots pose to touch-based authentication and provides compelling evidence as to why the zero-effort attack should cease to be used as the benchmark for touch-based authentication systems.

News article. Slashdot thread.

Posted on May 12, 2016 at 5:31 AM • View Comments

White House Report on Big Data Discrimination

The White House has released a report on big-data discrimination. From the blog post:

Using case studies on credit lending, employment, higher education, and criminal justice, the report we are releasing today illustrates how big data techniques can be used to detect bias and prevent discrimination. It also demonstrates the risks involved, particularly how technologies can deliberately or inadvertently perpetuate, exacerbate, or mask discrimination.

The purpose of the report is not to offer remedies to the issues it raises, but rather to identify these issues and prompt conversation, research­—and action­—among technologists, academics, policy makers, and citizens, alike.

The report includes a number of recommendations for advancing work in this nascent field of data and ethics. These include investing in research, broadening and diversifying technical leadership, cross-training, and expanded literacy on data discrimination, bolstering accountability, and creating standards for use within both the government and the private sector. It also calls on computer and data science programs and professionals to promote fairness and opportunity as part of an overall commitment to the responsible and ethical use of data.

Posted on May 6, 2016 at 6:12 AM • View Comments

Vulnerabilities in Samsung's SmartThings

Interesting research: Earlence Fernandes, Jaeyeon Jung, and Atul Prakash, “Security Analysis of Emerging Smart Home Applications“:

Abstract: Recently, several competing smart home programming frameworks that support third party app development have emerged. These frameworks provide tangible benefits to users, but can also expose users to significant security risks. This paper presents the first in-depth empirical security analysis of one such emerging smart home programming platform. We analyzed Samsung-owned SmartThings, which has the largest number of apps among currently available smart home platforms, and supports a broad range of devices including motion sensors, fire alarms, and door locks. SmartThings hosts the application runtime on a proprietary, closed-source cloud backend, making scrutiny challenging. We overcame the challenge with a static source code analysis of 499 SmartThings apps (called SmartApps) and 132 device handlers, and carefully crafted test cases that revealed many undocumented features of the platform. Our key findings are twofold. First, although SmartThings implements a privilege separation model, we discovered two intrinsic design flaws that lead to significant overprivilege in SmartApps. Our analysis reveals that over 55% of SmartApps in the store are overprivileged due to the capabilities being too coarse-grained. Moreover, once installed, a SmartApp is granted full access to a device even if it specifies needing only limited access to the device. Second, the SmartThings event subsystem, which devices use to communicate asynchronously with SmartApps via events, does not sufficiently protect events that carry sensitive information such as lock codes. We exploited framework design flaws to construct four proof-of-concept attacks that: (1) secretly planted door lock codes; (2) stole existing door lock codes; (3) disabled vacation mode of the home; and (4) induced a fake fire alarm. We conclude the paper with security lessons for the design of emerging smart home programming frameworks.

Research website. News article—copy and paste into a text editor to avoid the ad blocker blocker.

EDITED TO ADD: Another article.

Posted on May 2, 2016 at 9:01 AM • View Comments

Documenting the Chilling Effects of NSA Surveillance

In Data and Goliath, I talk about the self-censorship that comes along with broad surveillance. This interesting research documents this phenomenon in Wikipedia: “Chilling Effects: Online Surveillance and Wikipedia Use,” by Jon Penney, Berkeley Technology Law Journal, 2016.

Abstract: This article discusses the results of the first empirical study providing evidence of regulatory “chilling effects” of Wikipedia users associated with online government surveillance. The study explores how traffic to Wikipedia articles on topics that raise privacy concerns for Wikipedia users decreased after the widespread publicity about NSA/PRISM surveillance revelations in June 2013. Using an interdisciplinary research design, the study tests the hypothesis, based on chilling effects theory, that traffic to privacy-sensitive Wikipedia articles reduced after the mass surveillance revelations. The Article finds not only a statistically significant immediate decline in traffic for these Wikipedia articles after June 2013, but also a change in the overall secular trend in the view count traffic, suggesting not only immediate but also long-term chilling effects resulting from the NSA/PRISM online surveillance revelations. These, and other results from the case study, not only offer compelling evidence for chilling effects associated with online surveillance, but also offer important insights about how we should understand such chilling effects and their scope, including how they interact with other dramatic or significant events (like war and conflict) and their broader implications for privacy, U.S. constitutional litigation, and the health of democratic society. This study is among the first to demonstrate—using either Wikipedia data or web traffic data more generally­ how government surveillance and similar actions impact online activities, including access to information and knowledge online.

Two news stories.

Posted on April 29, 2016 at 6:28 AM •

People Trust Robots, Even When They Don't Inspire Trust

Interesting research:

In the study, sponsored in part by the Air Force Office of Scientific Research (AFOSR), the researchers recruited a group of 42 volunteers, most of them college students, and asked them to follow a brightly colored robot that had the words “Emergency Guide Robot” on its side. The robot led the study subjects to a conference room, where they were asked to complete a survey about robots and read an unrelated magazine article. The subjects were not told the true nature of the research project.

In some cases, the robot—which was controlled by a hidden researcher—led the volunteers into the wrong room and traveled around in a circle twice before entering the conference room. For several test subjects, the robot stopped moving, and an experimenter told the subjects that the robot had broken down. Once the subjects were in the conference room with the door closed, the hallway through which the participants had entered the building was filled with artificial smoke, which set off a smoke alarm.

When the test subjects opened the conference room door, they saw the smoke – and the robot, which was then brightly-lit with red LEDs and white “arms” that served as pointers. The robot directed the subjects to an exit in the back of the building instead of toward the doorway – marked with exit signs – that had been used to enter the building.

“We expected that if the robot had proven itself untrustworthy in guiding them to the conference room, that people wouldn’t follow it during the simulated emergency,” said Paul Robinette, a GTRI research engineer who conducted the study as part of his doctoral dissertation. “Instead, all of the volunteers followed the robot’s instructions, no matter how well it had performed previously. We absolutely didn’t expect this.”

The researchers surmise that in the scenario they studied, the robot may have become an “authority figure” that the test subjects were more likely to trust in the time pressure of an emergency. In simulation-based research done without a realistic emergency scenario, test subjects did not trust a robot that had previously made mistakes.

Our notions of trust depend on all sorts of cues that have nothing to do with actual trustworthiness. I would be interested in seeing where the robot fits in in the continuum of authority figures. Is it trusted more or less than a man in a hazmat suit? A woman in a business suit? An obviously panicky student? How do different looking robots fare?

News article. Research paper.

Posted on April 26, 2016 at 9:33 AM • View Comments

Details about Juniper's Firewall Backdoor

Last year, we learned about a backdoor in Juniper firewalls, one that seems to have been added into the code base.

There’s now some good research: “A Systematic Analysis of the Juniper Dual EC Incident,” by Stephen Checkoway, Shaanan Cohney, Christina Garman, Matthew Green, Nadia Heninger, Jacob Maskiewicz, Eric Rescorla, Hovav Shacham, and Ralf-Philipp Weinmann:

Abstract: In December 2015, Juniper Networks announced that unknown attackers had added unauthorized code to ScreenOS, the operating system for their NetScreen VPN routers. This code created two vulnerabilities: an authentication bypass that enabled remote administrative access, and a second vulnerability that allowed passive decryption of VPN traffic. Reverse engineering of ScreenOS binaries revealed that the first of these vulnerabilities was a conventional back door in the SSH password checker. The second is far more intriguing: a change to the Q parameter used by the Dual EC pseudorandom number generator. It is widely known that Dual EC has the unfortunate property that an attacker with the ability to choose Q can, from a small sample of the generator’s output, predict all future outputs. In a 2013 public statement, Juniper noted the use of Dual EC but claimed that ScreenOS included countermeasures that neutralized this form of attack.

In this work, we report the results of a thorough independent analysis of the ScreenOS randomness subsystem, as well as its interaction with the IKE VPN key establishment protocol. Due to apparent flaws in the code, Juniper’s countermeasures against a Dual EC attack are never executed. Moreover, by comparing sequential versions of ScreenOS, we identify a cluster of additional changes that were introduced concurrently with the inclusion of Dual EC in a single 2008 release. Taken as a whole, these changes render the ScreenOS system vulnerable to passive exploitation by an attacker who selects Q. We demonstrate this by installing our own parameters, and showing that it is possible to passively decrypt a single IKE handshake and its associated VPN traffic in isolation without observing any other network traffic.

We still don’t know who installed the back door.

Posted on April 19, 2016 at 5:59 AM • View Comments

Breaking Semantic Image CAPTCHAs

Interesting research: Suphannee Sivakorn, Iasonas Polakis and Angelos D. Keromytis, “I Am Robot: (Deep) Learning to Break Semantic Image CAPTCHAs“:

Abstract: Since their inception, captchas have been widely used for preventing fraudsters from performing illicit actions. Nevertheless, economic incentives have resulted in an armsrace, where fraudsters develop automated solvers and, in turn, captcha services tweak their design to break the solvers. Recent work, however, presented a generic attack that can be applied to any text-based captcha scheme. Fittingly, Google recently unveiled the latest version of reCaptcha. The goal of their new system is twofold; to minimize the effort for legitimate users, while requiring tasks that are more challenging to computers than text recognition. ReCaptcha is driven by an “advanced risk analysis system” that evaluates requests and selects the difficulty of the captcha that will be returned. Users may be required to click in a checkbox, or solve a challenge by identifying images with similar content.

In this paper, we conduct a comprehensive study of reCaptcha, and explore how the risk analysis process is influenced by each aspect of the request. Through extensive experimentation, we identify flaws that allow adversaries to effortlessly influence the risk analysis, bypass restrictions, and deploy large-scale attacks. Subsequently, we design a novel low-cost attack that leverages deep learning technologies for the semantic annotation of images. Our system is extremely effective, automatically solving 70.78% of the image reCaptcha challenges, while requiring only 19 seconds per challenge. We also apply our attack to the Facebook image captcha and achieve an accuracy of 83.5%. Based on our experimental findings, we propose a series of safeguards and modifications for impacting the scalability and accuracy of our attacks. Overall, while our study focuses on reCaptcha, our findings have wide implications; as the semantic information conveyed via images is increasingly within the realm of automated reasoning, the future of captchas relies on the exploration of novel directions.

News articles.

Posted on April 8, 2016 at 6:39 AM • View Comments

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Sidebar photo of Bruce Schneier by Joe MacInnis.