654 research outputs found
Privacy Preserving Optics Clustering
OPTICS is a well-known density-based clustering algorithm which uses DBSCAN theme without producing a clustering of a data set openly, but as a substitute, it creates an augmented ordering of that particular database which represents its density-based clustering structure. This resulted cluster-ordering comprises information which is similar to the density based clustering’s conforming to a wide range of parameter settings. The same algorithm can be applied in the field of privacy-preserving data mining, where extracting the useful information from data which is distributed over a network requires preservation of privacy of individuals’ information. The problem of getting the clusters of a distributed database is considered as an example of this algorithm, where two parties want to know their cluster numbers on combined database without revealing one party information to other party. This issue can be seen as a particular example of secure multi-party computation and such sort of issues can be solved with the assistance of proposed protocols in our work along with some standard protocols
Decentralized collaborative TTP free approach for privacy preservation in location based services
In recent trends, growth of location based services have been increased due to the large usage of cell phones, personal digital assistant and other devices like location based navigation, emergency services, location based social networking, location based advertisement, etc. Users are provided with important information based on location to the service provider that results the compromise with their personal information like user’s identity, location privacy etc. To achieve location privacy of the user, cryptographic technique is one of the best technique which gives assurance. Location based services are classified as Trusted Third Party (TTP) & without Trusted Third Party that uses cryptographic approaches. TTP free is one of the prominent approach in which it uses peer-to-peer model. In this approach, important users mutually connect with each other to form a network to work without the use of any person/server. There are many existing approaches in literature for privacy preserving location based services, but their solutions are at high cost or not supporting scalability. In this paper, our aim is to propose an approach along with algorithms that will help the location based services (LBS) users to provide location privacy with minimum cost and improve scalability
PALMAR: Towards Adaptive Multi-inhabitant Activity Recognition in Point-Cloud Technology
With the advancement of deep neural networks and computer vision-based Human
Activity Recognition, employment of Point-Cloud Data technologies (LiDAR,
mmWave) has seen a lot interests due to its privacy preserving nature. Given
the high promise of accurate PCD technologies, we develop, PALMAR, a
multiple-inhabitant activity recognition system by employing efficient signal
processing and novel machine learning techniques to track individual person
towards developing an adaptive multi-inhabitant tracking and HAR system. More
specifically, we propose (i) a voxelized feature representation-based real-time
PCD fine-tuning method, (ii) efficient clustering (DBSCAN and BIRCH), Adaptive
Order Hidden Markov Model based multi-person tracking and crossover ambiguity
reduction techniques and (iii) novel adaptive deep learning-based domain
adaptation technique to improve the accuracy of HAR in presence of data
scarcity and diversity (device, location and population diversity). We
experimentally evaluate our framework and systems using (i) a real-time PCD
collected by three devices (3D LiDAR and 79 GHz mmWave) from 6 participants,
(ii) one publicly available 3D LiDAR activity data (28 participants) and (iii)
an embedded hardware prototype system which provided promising HAR performances
in multi-inhabitants (96%) scenario with a 63% improvement of multi-person
tracking than state-of-art framework without losing significant system
performances in the edge computing device.Comment: Accepted in IEEE International Conference on Computer Communications
202
Unsupervised User Stance Detection on Twitter
We present a highly effective unsupervised framework for detecting the stance
of prolific Twitter users with respect to controversial topics. In particular,
we use dimensionality reduction to project users onto a low-dimensional space,
followed by clustering, which allows us to find core users that are
representative of the different stances. Our framework has three major
advantages over pre-existing methods, which are based on supervised or
semi-supervised classification. First, we do not require any prior labeling of
users: instead, we create clusters, which are much easier to label manually
afterwards, e.g., in a matter of seconds or minutes instead of hours. Second,
there is no need for domain- or topic-level knowledge either to specify the
relevant stances (labels) or to conduct the actual labeling. Third, our
framework is robust in the face of data skewness, e.g., when some users or some
stances have greater representation in the data. We experiment with different
combinations of user similarity features, dataset sizes, dimensionality
reduction methods, and clustering algorithms to ascertain the most effective
and most computationally efficient combinations across three different datasets
(in English and Turkish). We further verified our results on additional tweet
sets covering six different controversial topics. Our best combination in terms
of effectiveness and efficiency uses retweeted accounts as features, UMAP for
dimensionality reduction, and Mean Shift for clustering, and yields a small
number of high-quality user clusters, typically just 2--3, with more than 98\%
purity. The resulting user clusters can be used to train downstream
classifiers. Moreover, our framework is robust to variations in the
hyper-parameter values and also with respect to random initialization
A privacy preserving approach to energy theft detection in smart grids
A major challenge for utilities is energy theft, wherein malicious actors steal energy for financial gain. One such form of theft in the smart grid is the fraudulent amplification of energy generation measurements from DERs, such as photo-voltaics. It is important to detect this form of malicious activity, but in a way that ensures the privacy of customers. Not considering privacy aspects could result in a backlash from customers and a heavily curtailed deployment of services, for example. In this short paper, we present a novel privacy-preserving approach to the detection of manipulated DER generation measurements
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