Archive for the 'Mobile Computing' Category
March 14th, 2017, by Tim Finin, posted in Datamining, Machine Learning, Mobile Computing, Security
Prajit Kumar Das, Anupam Joshi and Tim Finin, App behavioral analysis using system calls, MobiSec: Security, Privacy, and Digital Forensics of Mobile Systems and Networks, IEEE Conference on Computer Communications Workshops, May 2017.
System calls provide an interface to the services made available by an operating system. As a result, any functionality provided by a software application eventually reduces to a set of fixed system calls. Since system calls have been used in literature, to analyze program behavior we made an assumption that analyzing the patterns in calls made by a mobile application would provide us insight into its behavior. In this paper, we present our preliminary study conducted with 534 mobile applications and the system calls made by them. Due to a rising trend of mobile applications providing multiple functionalities, our study concluded, mapping system calls to functional behavior of a mobile application was not straightforward. We use Weka tool and manually annotated application behavior classes and system call features in our experiments to show that using such features achieves mediocre F1-measure at best, for app behavior classification. Thus leading to the conclusion that system calls were not sufficient features for app behavior classification.
February 27th, 2017, by Tim Finin, posted in Mobile Computing, Privacy, Security
Context-Dependent Privacy and Security Management on Mobile Devices
10:00am Tuesday, 27 February, 2017
Security and privacy of mobile devices is a challenging research domain. A prominent aspect of this research focuses on discovering software vulnerabilities for mobile operating systems and mobile apps. The other aspect of research focuses on user privacy and using feedback, generates privacy profiles for controlling data privacy. Profile based or role-based security can be restrictive as they require prior definition of such roles or profiles. As a result, it is better to use attribute-based access control and let the attributes define granularity of policy definition. This problem may thus be defined as, a security and privacy personalization problem. A critical issue in the process of capturing personalized policy is one of creating a system that is adaptive and knows when user’s preferences have been captured. Presented in this work you will learn about Mithril, a framework for capturing user access control policies that are fine-grained, context-sensitive and are represented using Semantic Web technologies and thereby manages access control decisions for user data on mobile devices. Violation metric has been used in this work as a measure to determine system state. A hierarchical context ontology has been used to define fine-grained access control policies and simplifying the process of policy modification for a user. A secondary goal of this research was to determine behavioral traits of mobile applications with a goal to detect outlier applications. Some preliminary research on this topic will also be discussed.
November 8th, 2016, by Tim Finin, posted in cybersecurity, Ebiquity, Mobile Computing, Policy, Privacy
In this week’s ebiquity meeting (11:30 8 Nov. 2016) Prajit Das will present his work on capturing policies for fine-grained access control on mobile devices.
As of 2016, there are more mobile devices than humans on earth. Today, mobile devices are a critical part of our lives and often hold sensitive corporate and personal data. As a result, they are a lucrative target for attackers, and managing data privacy and security on mobile devices has become a vital issue. Existing access control mechanisms in most devices are restrictive and inadequate. They do not take into account the context of a device and its user when making decisions. In many cases, the access granted to a subject should change based on context of a device. Such fine-grained, context-sensitive access control policies have to be personalized too. In this paper, we present the Mithril system, that uses policies represented in Semantic Web technologies and captured using user feedback, to handle access control on mobile devices. We present an iterative feedback process to capture user specific policy. We also present a policy violation metric that allows us to decide when the capture process is complete.
March 27th, 2016, by Tim Finin, posted in cybersecurity, Machine Learning, Mobile Computing, Security
Down the rabbit hole: An Android system call study
Prajit Kumar Das
10:30 am, Monday, March 28, 2016 ITE 346
App permissions and application sandboxing are the fundamental security mechanisms that protects user data on mobile platforms. We have worked on permission analytics before and come to a conclusion that just studying an app’s requested access rights (permissions) isn’t enough to understand potential data breaches. Techniques like privilege escalation have been previously used to gain further access to user and her data on mobile platforms like Android. Static code analysis and dynamic code execution may be studied to gather further insight into an app’s behavior. However, there is a need to study such a behavior at the lowest level of code execution and that is system calls. The system call is the fundamental interface between an application and the Linux kernel. In our current project, we are studying system calls made by apps for gathering a better understanding of their behavior.
October 25th, 2015, by Tim Finin, posted in Pervasive Computing, Security
In this week’s ebiquity meeting (10:30am Monday, 26 October 2015 in ITE346 at UMBC), Sandeep Nair will talk about his research on securing the cyber-physical systems in modern vehicles.
Vehicles changed from being just mechanical devices which will just obey the commands to a smarter Sensor-ECU-Actuator systems which sense the surroundings and take necessary smart actions. A modern car has around forty to hundred different ECU’s, possibly communicating, to make intelligent decisions. But recently, there is a lot of buzz in the research community on hacking and taking control of vehicles. These literature describe and document the different ways to take control of vehicles. In this talk, we will first discuss what makes this kind of hacking possible? Then we will continue with different logical ways to do this and discuss some proposed mechanisms to protect it. We then propose a context aware mechanism which can detect these unsafe behaviors in the vehicle and describe the challenges associated with them.
June 8th, 2015, by Tim Finin, posted in AI, KR, Machine Learning, Mobile Computing, Ontologies
The NSF-sponsored Platys project explored the idea that places are more than just GPS coordinates. They are concepts rich with semantic information, including people, activities, roles, functions, time and purpose. Our mobile phones can learn to recognize the places we are in and use information about them to provide better services.
Laura Zavala, Pradeep K. Murukannaiah, Nithyananthan Poosamani, Tim Finin, Anupam Joshi, Injong Rhee and Munindar P. Singh, Platys: From Position to Place-Oriented Mobile Computing, AI Magazine, v36, n2, 2015.
The Platys project focuses on developing a high-level, semantic notion of location called place. A place, unlike a geospatial position, derives its meaning from a user’s actions and interactions in addition to the physical location where it occurs. Our aim is to enable the construction of a large variety of applications that take advantage of place to render relevant content and functionality and, thus, improve user experience. We consider elements of context that are particularly related to mobile computing. The main problems we have addressed to realize our place-oriented mobile computing vision are representing places, recognizing places, and engineering place-aware applications. We describe the approaches we have developed for addressing these problems and related subproblems. A key element of our work is the use of collaborative information sharing where users’ devices share and integrate knowledge about places. Our place ontology facilitates such collaboration. Declarative privacy policies allow users to specify contextual features under which they prefer to share or not share their information.
January 27th, 2015, by Prajit Kumar Das, posted in Microsoft, Pervasive Computing, Privacy, Technology, Technology Impact, Wearable Computing
In this post we will talk about certain User Interface (UI) technological advances that we are observing at the moment. One such development was revealed in a recent media event conducted by Microsoft, where they announced the Microsoft HoloLens, a computing platform which achieves seamless connection between the digital and the physical world, quite similar to the experience referred to in certain movies in the past.
It is interesting to note that the design of the HoloLens device looks so similar to something we have seen before.
Even the vision of holographic computing and users interacting with such interfaces isn’t a new one. The 2002 movie “The first $20 million is always the hardest” was possibly the first time we saw how such a futuristic technology might look like.
How did we reach here? A brief discussion on UIs…
User interfaces have always been an important aspect of computers. In its early days computers had a monochromatic screen (or at-most a duo-chromatic screen). A user would type in commands into the screen and computers would execute said commands. Since the commands would be entered in a single or a series of lines, this interface was called the Command-Line Interface (CLI).
Command Line based UI
Such an interface was not particularly intuitive as you had to know the list of commands that would fulfill a certain task. Albeit a certain group of individuals i.e. geeks and some computer programmers, like me, prefer such an interface owing to its clean and distraction free nature. However, owing to the learning curve of CLIs, researchers at Stanford Research Institute and Xerox PARC research center invented a new User interface called the Graphical User Interface (GUI). There were a few variations of the GUIs for example the point and click type also known as WIMP (windows, icons, menus, pointer) UI created at the Xerox PARC research center and made popular by Apple through it’s Macintosh operating systems
Apple’s Macintosh UI
And also adopted by Microsoft in its Windows operating systems
Microsoft’s Windows UI
Some early versions even included a textual user interface with programs which had menus that could be parsed using a keyboard instead of a mouse.
Early textual menu based UI
Eventually new avenues were created for UI research. Continuing onwards from textual interfaces to the WIMP interfaces to the world wide web where objects on the web became entities accessible through a Uniform Resource Identifier (URI). Such an entity could possibly have Semantics associated with them too (as defined by Web 2.0). However, with the advent of mobile smart-phones we saw a completely different class of user interfaces. The touch-based user interfaces and its more evolved cousin the multi-touch systems which allowed gesture based interactions.
Touch and gesture based UI
This was the first time in computing history that humans were able to directly interact with an object on their device with their hands instead of using an input device. The experience was immersive but yet these objects had not entered into the real world. We were on precipice of a revolution in computing.
This revolution was the mainstream launch of Wearable Technology and Virtual/Augmented Reality and Optical Head Mounted Display devices with the creation of devices like the Oculus Rift, Google Glass and EyeTap among others. These devices allowed voice inputs and created a virtual or an augmented reality world for it’s user. Microsoft too was working on gesture based interactions with the Kinect device and research in the Natural User Interface (NUI) field. Couple of interesting works worthy of taking a look from this revolution are listed below.
This talk by John Underkoffler demos a UI that we saw in the movie Minority Report. He talks about the spatial aspect of how humans interact with their world and how computers might be able to help us better if we could do the same with our computers.
Here Pranav Mistry, currently the Head of the Think Tank Team and Director of Research of Samsung Research America, speaks of SixthSense. A new paradigm in computing that allows interaction between the real world and the digital world. All these works were knocking on the doors of a computer as we saw in the 2002 movie mentioned earlier, a real life holographic computer. Enter Microsoft HoloLens!
What is Microsoft HoloLens?
Microsoft HoloLens is an augmented reality computing platform. As per the review from Forbes.com this device has taken a step beyond current work by adding to the world around its user, virtual holograms, rather than putting the user in a completely virtual environment. This device has launched a new platform of software development, i.e. Holographic apps. As well as, the device has created a scope for hardware research and development, as it requires new components like the Holographic Processing Unit or HPU. Visualization and sharing of ideas and interaction with the real world can now be done as envisioned in the TED talk by Pranav Mistry. A more natural way of interacting with digital content as envisioned in the works above are a reality now. The device tracks its user’s movements in an environment. It detects what a person is looking at and transforms the visual field by overlaying 3D objects on top of that.
What kind of applications can we expect to be developed for HoloLens?
When the touch UI became a reality developers had to change the way they worked on software. Direct object interactions as shown above had to be programmed into their applications. Apps for HoloLens would similarly need to handle use-cases of interactions involving voice commands and gesture recognition. The common ideas and their corresponding research implication that come to mind include:
- Looking up a grocery list when you enter the grocery store (context aware)
HoloLens Environment overlaid with lists
- Recording important events automatically (context aware computing)
- Recognizing people in a party (social media and privacy)
- Taking down notes, writing emails using voice commands (natural language understanding)
- Searching for “stuff” around us (nlp, data analytics, semantic web, context aware computing)
- Playing 3D games (animation and graphics)
HoloLens Environment overlaid with 3D Games
- Making sure your battery doesn’t run out (systems, hardware)
- Virtual work environments (systems)
Virtual Work Environments through HoloLens
- Teaching virtual classrooms (systems)
Why or how could it fail?
Are there any obvious pitfalls that we are not thinking about? We can be rest assured that researchers are already looking at ways this venture can fail and for Microsoft’s own good we can be certain they have a list of ways they think this might go and if there are any flaws they are surely working on fixing them. However, as a researcher in the mobile field with a bit of experience with the Google Glass, we can try to list some of the possible pitfalls of a AR/VR device. The HoloLens being a tetherless, Augmented Virtual Reality (AVR) device could possibly suffer from some of these pitfalls too. The reader should understand that we are not claiming any of the following to be scientifically provable because these are merely empirical observations.
- The first thing that worried us while using the Google Glass was that it would sometimes cause us headaches after using it for couple of hours. We have not researched the implications of using the device by any other person so this is and observation from experience. Therefore one concern could be regarding the health impact on a human being with prolonged usage of an AVR device.
- The second thing that was noticed with the Google Glass was how that the device heated up fast. We know from experience that computers do get hot. For example when we play a game they get hot or we do a lot of complex computations they get hot. An AVR device which is being used for playing games will most probably get hot too. At least the Google Glass did after recording a video. Here we are concerned about the heat dissipation and its health impact on the user.
- The third observation that we made was that the Google Glass, showed significant sluggishness when it tried to accomplish computation heavy tasks. Will the HoloLens device be able to keep up with all the computations needed for, say, playing a 3D game?
- The fourth concern is regarding battery capacity. The HoloLens is advertised as a device with no wires, cords or tethers. Anyone who has used a smartphone ever knows the issues of the battery on the devices running out within a day or even half a day. Will the HoloLens be able to carry a charge for long or will it require constant charging?
- The fifth concern that we had was regarding privacy. The Google Glass has faced quite a few privacy concerns because it can readily take pictures using a simple voice command or even a non-verbal command like a ‘wink’. We have worked on this issue as part of our research product FaceBlock. Will the HoloLens create such concerns as this device too has front facing cameras that are capturing a user’s environment and projecting an augmented virtual world to the user.
The above lists of possible issues and probable application areas are not exhaustive in anyway. There will be numerous other scenarios and ways we can work on this new computing platform. There will probably be a multitude of issues with such a new and revolutionary platform. However, the hybrid of augmented and virtual reality has just started taking small steps now. With invention of devices like the Microsoft HoloLens, Google Glass, Oculus Rift, EyeTap etc. we can look forward to an exciting period in the future of Computing for Augmented Virtual Reality.
December 15th, 2014, by Tim Finin, posted in Mobile Computing, OWL, Policy, RDF, Semantic Web
Roberto Yus, Primal Pappachan, Prajit Das, Tim Finin, Anupam Joshi, and Eduardo Mena, Semantics for Privacy and Shared Context, Workshop on Society, Privacy and the Semantic Web-Policy and Technology, held at Int. Semantic Web Conf., Oct. 2014.
Capturing, maintaining, and using context information helps mobile applications provide better services and generates data useful in specifying information sharing policies. Obtaining the full benefit of context information requires a rich and expressive representation that is grounded in shared semantic models. We summarize some of our past work on representing and using context models and briefly describe Triveni, a system for cross-device context discovery and enrichment. Triveni represents context in RDF and OWL and reasons over context models to infer additional information and detect and resolve ambiguities and inconsistencies. A unique feature, its ability to create and manage “contextual groups” of users in an environment, enables their members to share context information using wireless ad-hoc networks. Thus, it enriches the information about a user’s context by creating mobile ad hoc knowledge networks.
September 19th, 2014, by Tim Finin, posted in Mobile Computing, OWL, RDF, Semantic Web, Wearable Computing
Primal Pappachan, Roberto Yus, Anupam Joshi and Tim Finin, Rafiki: A Semantic and Collaborative Approach to Community Health-Care in Underserved Areas, 10th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing, 22-15 October2014, Miami.
Community Health Workers (CHWs) act as liaisons between health-care providers and patients in underserved or un-served areas. However, the lack of information sharing and training support impedes the effectiveness of CHWs and their ability to correctly diagnose patients. In this paper, we propose and describe a system for mobile and wearable computing devices called Rafiki which assists CHWs in decision making and facilitates collaboration among them. Rafiki can infer possible diseases and treatments by representing the diseases, their symptoms, and patient context in OWL ontologies and by reasoning over this model. The use of semantic representation of data makes it easier to share knowledge related to disease, symptom, diagnosis guidelines, and patient demography, between various personnel involved in health-care (e.g., CHWs, patients, health-care providers). We describe the Rafiki system with the help of a motivating community health-care scenario and present an Android prototype for smart phones and Google Glass.
March 27th, 2014, by Prajit Kumar Das, posted in Ebiquity, Google, Mobile Computing, Policy, Semantic Web, Social, Wearable Computing
If you are a Google Glass user, you might have been greeted with concerned looks or raised eyebrows at public places. There has been a lot of chatter in the “interweb” regarding the loss of privacy that results from people taking your pictures with Glass without notice. Google Glass has simplified photography but as what happens with revolutionary technology people are worried about the potential misuse.
FaceBlock helps to protect the privacy of people around you by allowing them to specify whether or not to be included in your pictures. This new application developed by the joint collaboration between researchers from the Ebiquity Research Group at University of Maryland, Baltimore County and Distributed Information Systems (DIS) at University of Zaragoza (Spain), selectively obscures the face of the people in pictures taken by Google Glass.
Comfort at the cost of Privacy?
As the saying goes, “The best camera is the one that’s with you”. Google Glass suits this description as it is always available and can take a picture with a simple voice command (“Okay Glass, take a picture”). This allows users to capture spontaneous life moments effortlessly. On the flip side, this raises significant privacy concerns as pictures can taken without one’s consent. If one does not use this device responsibly, one risks being labelled a “Glasshole”. Quite recently, a Google Glass user was assaulted by the patrons who objected against her wearing the device inside the bar. The list of establishments which has banned Google Glass within their premises is growing day by day. The dos and donts for Glass users released by Google is a good first step but it doesn’t solve the problem of privacy violation.
Privacy-Aware pictures to the rescue
FaceBlock takes regular pictures taken by your smartphone or Google Glass as input and converts it into privacy-aware pictures. This output is generated by using a combination of Face Detection and Face Recognition algorithms. By using FaceBlock, a user can take a picture of herself and specify her policy/rule regarding pictures taken by others (in this case ‘obscure my face in pictures from strangers’). The application would automatically generate a face identifier for this picture. The identifier is a mathematical representation of the image. To learn more about the working on FaceBlock, you should watch the following video.
Using Bluetooth, FaceBlock can automatically detect and share this policy with Glass users near by. After receiving this face identifier from a nearby user, the following post processing steps happen on Glass as shown in the images.
What promises does it hold?
FaceBlock is a proof of concept implementation of a system that can create privacy-aware pictures using smart devices. The pervasiveness of privacy-aware pictures could be a right step towards balancing privacy needs and comfort afforded by technology. Thus, we can get the best out of Wearable Technology without being oblivious about the privacy of those around you.
FaceBlock is part of the efforts of Ebiquity and SID in building systems for preserving user privacy on mobile devices. For more details, visit http://face-block.me
March 9th, 2013, by Tim Finin, posted in Mobile Computing, Privacy
Memoto is a $279 lifelogging camera takes a geotagged photo every 30 seconds, holds 6K photos, and runs for several days without recharging. The company producing Memoto is a Swedish company intially funded via kickstarter and expects to start shipping the wearable camera in April 2013. The company will also offer “safe and secure infinite photo storage at a flat monthly fee, which will always be a lot more affordable than hard drives.”
The lifelogging idea has been around for many years but has yet to become propular. One reason is privacy concerns. DARPA’s IPTO office, for example, started a LifeLog program in 2004 which was almost immediately canceled after criticism from civil libertarians concerning the privacy implications of the system.
February 28th, 2013, by Tim Finin, posted in Gadgets, Mobile Computing, Wearable Computing
UMBC CSEE department members submitted a number of #ifihadglass posts hoping to get an invitation to pre-order a Google Glass device. Several came from the UMBC Ebiquity Lab including this one that builds on our work with context-aware mobile phones.
Reports are that as many as 8,000 of the submitted ideas will be invited to the first round of pre-orders. To get a rough idea of our odds, I tried using Google and Bing searches to estimate the number of submissions. A general search for pages with the #ifihadglass tag returned 249K hits on Google. Of these 21K were from twitter and less than 4K from Google+. I’m not sure which of the twitter and Google+ posts get indexed and how long it takes, but I do know that our entry above did not show up in the results. Bing reported 171K results for a search on the hash tag, but our post was not among them. I tried the native search services on both Twitter and Google+, but these are oriented toward delivering a stream of new results and neither gives an estimate of the total number of results. I suppose one could do this for Twitter using their custom search API, but even then I am not sure how accurately one could estimate the total number of matching tweets.
Can anyone suggest how to easily estimate the number of #ifihadglass posts on twitter and Google+?
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