2,927 research outputs found
Mobile sensor data anonymization
Data from motion sensors such as accelerometers and gyroscopes embedded in our devices can reveal secondary undesired, private information about our activities. This information can be used for malicious purposes such as user identification by application developers. To address this problem, we propose a data transformation mechanism that enables a device to share data for specific applications (e.g.~monitoring their daily activities) without revealing private user information (e.g.~ user identity). We formulate this anonymization process based on an information theoretic approach and propose a new multi-objective loss function for training convolutional auto-encoders~(CAEs) to provide a practical approximation to our anonymization problem. This effective loss function forces the transformed data to minimize the information about the user's identity, as well as the data distortion to preserve application-specific utility. Our training process regulates the encoder to disregard user-identifiable patterns and tunes the decoder to shape the final output independently of users in the training set. Then, a trained CAE can be deployed on a user's mobile device to anonymize sensor data before sharing with an app, even for users who are not included in the training dataset. The results, on a dataset of 24 users for activity recognition, show a promising trade-off on transformed data between utility and privacy, with an accuracy for activity recognition over 92%, while reducing the chance of identifying a user to less than 7%
Mobile sensor data anonymization
Motion sensors such as accelerometers and gyroscopes measure
the instant acceleration and rotation of a device, in three dimensions. Raw data streams from motion sensors embedded in portable
and wearable devices may reveal private information about users
without their awareness. For example, motion data might disclose
the weight or gender of a user, or enable their re-identification. To
address this problem, we propose an on-device transformation of
sensor data to be shared for specific applications, such as monitoring selected daily activities, without revealing information that
enables user identification. We formulate the anonymization problem using an information-theoretic approach and propose a new
multi-objective loss function for training deep autoencoders. This
loss function helps minimizing user-identity information as well
as data distortion to preserve the application-specific utility. The
training process regulates the encoder to disregard user-identifiable
patterns and tunes the decoder to shape the output independently of
users in the training set. The trained autoencoder can be deployed
on a mobile or wearable device to anonymize sensor data even
for users who are not included in the training dataset. Data from
24 users transformed by the proposed anonymizing autoencoder
lead to a promising trade-off between utility and privacy, with an
accuracy for activity recognition above 92% and an accuracy for
user identification below 7
Energy efficient privacy preserved data gathering in wireless sensor networks having multiple sinks
Wireless sensor networks (WSNs) generally have a many-to-one structure so that event information flows from sensors to a unique sink. In recent WSN applications, many-tomany structures are evolved due to need for conveying collected event information to multiple sinks at the same time. This study proposes an anonymity method bases on k-anonymity for preventing record disclosure of collected event information in WSNs. Proposed method takes the anonymity requirements of multiple sinks into consideration by providing different levels of privacy for each destination sink. Attributes, which may identify of an event owner, are generalized or encrypted in order to
meet the different anonymity requirements of sinks. Privacy guaranteed event information can be multicasted to all sinks instead of sending to each sink one by one. Since minimization of energy consumption is an important design criteria for WSNs, our method enables us to multicast the same event information
to multiple sinks and reduce energy consumption
Privacy-enhancing Aggregation of Internet of Things Data via Sensors Grouping
Big data collection practices using Internet of Things (IoT) pervasive
technologies are often privacy-intrusive and result in surveillance, profiling,
and discriminatory actions over citizens that in turn undermine the
participation of citizens to the development of sustainable smart cities.
Nevertheless, real-time data analytics and aggregate information from IoT
devices open up tremendous opportunities for managing smart city
infrastructures. The privacy-enhancing aggregation of distributed sensor data,
such as residential energy consumption or traffic information, is the research
focus of this paper. Citizens have the option to choose their privacy level by
reducing the quality of the shared data at a cost of a lower accuracy in data
analytics services. A baseline scenario is considered in which IoT sensor data
are shared directly with an untrustworthy central aggregator. A grouping
mechanism is introduced that improves privacy by sharing data aggregated first
at a group level compared as opposed to sharing data directly to the central
aggregator. Group-level aggregation obfuscates sensor data of individuals, in a
similar fashion as differential privacy and homomorphic encryption schemes,
thus inference of privacy-sensitive information from single sensors becomes
computationally harder compared to the baseline scenario. The proposed system
is evaluated using real-world data from two smart city pilot projects. Privacy
under grouping increases, while preserving the accuracy of the baseline
scenario. Intra-group influences of privacy by one group member on the other
ones are measured and fairness on privacy is found to be maximized between
group members with similar privacy choices. Several grouping strategies are
compared. Grouping by proximity of privacy choices provides the highest privacy
gains. The implications of the strategy on the design of incentives mechanisms
are discussed
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Generic system architecture for context-aware, distributed recommendation
In the existing literature on recommender systems, it is difficult to find an architecture for large-scale implementation. Often, the architectures proposed in papers are specific to an algorithm implementation or a domain. Thus, there is no clear architectural starting point for a new recommender system. This paper presents an architecture blueprint for a context-aware recommender system that provides scalability, availability, and security for its users. The architecture also contributes the dynamic ability to switch between single-device (offline), client-server (online), and fully distributed implementations. From this blueprint, a new recommender system could be built with minimal design and implementation effort regardless of the application.Electrical and Computer Engineerin
Exploratory study to explore the role of ICT in the process of knowledge management in an Indian business environment
In the 21st century and the emergence of a digital economy, knowledge and the knowledge base economy are rapidly growing. To effectively be able to understand the processes involved in the creating, managing and sharing of knowledge management in the business environment is critical to the success of an organization. This study builds on the previous research of the authors on the enablers of knowledge management by identifying the relationship between the enablers of knowledge management and the role played by information communication technologies (ICT) and ICT infrastructure in a business setting. This paper provides the findings of a survey collected from the four major Indian cities (Chennai, Coimbatore, Madurai and Villupuram) regarding their views and opinions about the enablers of knowledge management in business setting. A total of 80 organizations participated in the study with 100 participants in each city. The results show that ICT and ICT infrastructure can play a critical role in the creating, managing and sharing of knowledge in an Indian business environment
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