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- Keyword
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- Privacy (6)
- Mhealth (4)
- Security (4)
- Wireless (4)
- Healthcare (3)
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- Network (3)
- Ubicomp (2)
- Anonymity (1)
- Anonymization (1)
- Female (1)
- Humans (1)
- Male (1)
- Methods (1)
- Mobile computing (1)
- Mobility characteristics (1)
- Nonlinear dynamics (1)
- Perception (1)
- Photography (1)
- Physiology (1)
- Sanitization (1)
- Sensors (1)
- User classification (1)
- Wifi (1)
- Wireless network trace study (1)
Articles 1 - 11 of 11
Full-Text Articles in Physical Sciences and Mathematics
A Perceptual Metric For Photo Retouching, Eric Kee, Hany Farid
A Perceptual Metric For Photo Retouching, Eric Kee, Hany Farid
Dartmouth Scholarship
In recent years, advertisers and magazine editors have been widely criticized for taking digital photo retouching to an extreme. Impossibly thin, tall, and wrinkle- and blemish-free models are routinely splashed onto billboards, advertisements, and magazine covers. The ubiquity of these unrealistic and highly idealized images has been linked to eating disorders and body image dissatisfaction in men, women, and children. In response, several countries have considered legislating the labeling of retouched photos. We describe a quantitative and perceptually meaningful metric of photo retouching. Photographs are rated on the degree to which they have been digitally altered by explicitly modeling and …
Adapt-Lite: Privacy-Aware, Secure, And Efficient Mhealth Sensing, Shrirang Mare, Jacob Sorber, Minho Shin, Cory Cornelius, David Kotz
Adapt-Lite: Privacy-Aware, Secure, And Efficient Mhealth Sensing, Shrirang Mare, Jacob Sorber, Minho Shin, Cory Cornelius, David Kotz
Dartmouth Scholarship
As healthcare in many countries faces an aging population and rising costs, mobile sensing technologies promise a new opportunity. Using mobile health (mHealth) sensing, which uses medical sensors to collect data about the patients, and mobile phones to act as a gateway between sensors and electronic health record systems, caregivers can continuously monitor the patients and deliver better care. Although some work on mHealth sensing has addressed security, achieving strong security and privacy for low-power sensors remains a challenge. \par We make three contributions. First, we propose Adapt-lite, a set of two techniques that can be applied to existing wireless …
Adaptive Security And Privacy For Mhealth Sensing, Shrirang Mare, Jacob Sorber, Minho Shin, Cory Cornelius, David Kotz
Adaptive Security And Privacy For Mhealth Sensing, Shrirang Mare, Jacob Sorber, Minho Shin, Cory Cornelius, David Kotz
Dartmouth Scholarship
As healthcare in many countries faces an aging population and rising costs, mobile Health (mHealth) sensing technologies promise a new opportunity. However, the privacy concerns associated with mHealth sensing are a limiting factor for their widespread adoption. The use of wireless body area networks pose a particular challenge. Although there exist protocols that provide a secure and private communication channel between two devices, the large transmission overhead associated with these protocols limit their application to low-power mHealth sensing devices. We propose an adaptive security model that enables use of privacy-preserving protocols in low-power mHealth sensing by reducing the network overhead …
Short Paper: The Netsani Framework For Analysis And Fine-Tuning Of Network Trace Sanitization, Phil Fazio, Keren Tan, Jihwang Yeo, David Kotz
Short Paper: The Netsani Framework For Analysis And Fine-Tuning Of Network Trace Sanitization, Phil Fazio, Keren Tan, Jihwang Yeo, David Kotz
Dartmouth Scholarship
Anonymization is critical prior to sharing wireless-network traces within the research community, to protect both personal and organizational sensitive information from disclosure. One difficulty in anonymization, or more generally, sanitization, is that users lack information about the quality of a sanitization result, such as how much privacy risk a sanitized trace may expose, and how much research utility the sanitized trace may retain. We propose a framework, NetSANI, that allows users to analyze and control the privacy/utility tradeoff in network sanitization. NetSANI can accommodate most of the currently available privacy and utility metrics for network trace sanitization. This framework provides …
Recognizing Whether Sensors Are On The Same Body, Cory Cornelius, David Kotz
Recognizing Whether Sensors Are On The Same Body, Cory Cornelius, David Kotz
Dartmouth Scholarship
As personal health sensors become ubiquitous, we also expect them to become interoperable. That is, instead of closed, end-to-end personal health sensing systems, we envision standardized sensors wirelessly communicating their data to a device many people already carry today, the cellphone. In an open personal health sensing system, users will be able to seamlessly pair off-the-shelf sensors with their cellphone and expect the system to ıt just work. However, this ubiquity of sensors creates the potential for users to accidentally wear sensors that are not necessarily paired with their own cellphone. A husband, for example, might mistakenly wear a heart-rate …
Privacy Analysis Of User Association Logs In A Large-Scale Wireless Lan, Keren Tan, Guanhua Yan, Jihwang Yeo, David Kotz
Privacy Analysis Of User Association Logs In A Large-Scale Wireless Lan, Keren Tan, Guanhua Yan, Jihwang Yeo, David Kotz
Dartmouth Scholarship
User association logs collected from a large-scale wireless LAN record where and when a user has used the network. Such information plays an important role in wireless network research. One concern of sharing these data with other researchers, however, is that the logs pose potential privacy risks for the network users. Today, the common practice in sanitizing these data before releasing them to the public is to anonymize users' sensitive information, such as their devices' MAC addresses and their exact association locations. In this work, we aim to study whether such sanitization measures are sufficient to protect user privacy. By …
Catch, Clean, And Release: A Survey Of Obstacles And Opportunities For Network Trace Sanitization, Keren Tan, Jihwang Yeo, Michael E. Locasto, David Kotz
Catch, Clean, And Release: A Survey Of Obstacles And Opportunities For Network Trace Sanitization, Keren Tan, Jihwang Yeo, Michael E. Locasto, David Kotz
Dartmouth Scholarship
Network researchers benefit tremendously from access to traces of production networks, and several repositories of such network traces exist. By their very nature, these traces capture sensitive business and personal activity. Furthermore, network traces contain significant operational information about the target network, such as its structure, identity of the network provider, or addresses of important servers. To protect private or proprietary information, researchers must “sanitize” a trace before sharing it. \par In this chapter, we survey the growing body of research that addresses the risks, methods, and evaluation of network trace sanitization. Research on the risks of network trace sanitization …
Social Network Analysis Plugin (Snap) For Mesh Networks, Soumendra Nanda, David Kotz
Social Network Analysis Plugin (Snap) For Mesh Networks, Soumendra Nanda, David Kotz
Dartmouth Scholarship
In a network, bridging nodes are those nodes that from a topological perspective, are strategically located between highly connected regions of nodes. Thus, they have high values of the Bridging Centrality (BC) metric. We recently introduced the Localized Bridging Centrality (LBC) metric, which can identify such nodes via distributed computation, yet has an accuracy equal to that of the centralized BC metric. The LBC and BC metrics are based on the Social Network Analysis (SNA) metric "betweenness centrality". We now introduce a new SNA metric that is more suitable for use in wireless mesh networks: the Localized Load-aware Bridging Centrality …
Identifying Unusual Days, Minkyong Kim, David Kotz
Identifying Unusual Days, Minkyong Kim, David Kotz
Dartmouth Scholarship
Pervasive applications such as digital memories or patient monitors collect a vast amount of data. One key challenge in these systems is how to extract interesting or unusual information. Because users cannot anticipate their future interests in the data when the data is stored, it is hard to provide appropriate indexes. As location-tracking technologies, such as global positioning system, have become ubiquitous, digital cameras or other pervasive systems record location information along with the data. In this paper, we present an automatic approach to identify unusual data using location information. Given the location information, our system identifies unusual days, that …
Anonysense: A System For Anonymous Opportunistic Sensing, Minho Shin, Cory Cornelius, Dan Peebles, Apu Kapadia, David Kotz, Nikos Triandopoulos
Anonysense: A System For Anonymous Opportunistic Sensing, Minho Shin, Cory Cornelius, Dan Peebles, Apu Kapadia, David Kotz, Nikos Triandopoulos
Dartmouth Scholarship
We describe AnonySense, a privacy-aware system for realizing pervasive applications based on collaborative, opportunistic sensing by personal mobile devices. AnonySense allows applications to submit sensing \emphtasks\/ to be distributed across participating mobile devices, later receiving verified, yet anonymized, sensor data \emphreports\/ back from the field, thus providing the first secure implementation of this participatory sensing model. We describe our security goals, threat model, and the architecture and protocols of AnonySense. We also describe how AnonySense can support extended security features that can be useful for different applications. We evaluate the security and feasibility of AnonySense through security analysis and prototype …
A Threat Taxonomy For Mhealth Privacy, David Kotz
A Threat Taxonomy For Mhealth Privacy, David Kotz
Dartmouth Scholarship
Networked mobile devices have great potential to enable individuals (and their physicians) to better monitor their health and to manage medical conditions. In this paper, we examine the privacy-related threats to these so-called \emphmHealth\/ technologies. We develop a taxonomy of the privacy-related threats, and discuss some of the technologies that could support privacy-sensitive mHealth systems. We conclude with a brief summary of research challenges.