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Privacy Preserving Nearest Neighbor Search
[chapter]
2009
Machine Learning in Cyber Trust
Preserved Fulltext
We show how this algorithm can be used in three important data mining algorithms, namely LOF outlier detection, SNN clustering, and kNN classification. ...
Privacy preserving data mining techniques have been developed to address this issue by providing mechanisms to mine the data while giving certain privacy guarantees. ...
LOF Outlier Detection LOF outlier detection [2] is a method of outlier detection that relies on relative densities of data points. ...
doi:10.1007/978-0-387-88735-7_10
fatcat:obtqow5n2rcftgnfquik5jtnji
PCOR: Private Contextual Outlier Release via Differentially Private Search
[article]
2021
arXiv
pre-print
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Outlier detection plays a significant role in various real world applications such as intrusion, malfunction, and fraud detection. ...
Hence, the first major challenge is that the privacy technique must preserve the validity of the context for each record. ...
The last sample in the stack is the final privacy preserving answer. Proof. ...
arXiv:2103.05173v1
fatcat:bm2r3fhflbhkbnvwsrvl6b5a2i
An Analysis of ML-Based Outlier Detection from Mobile Phone Trajectories
2022
Future Internet
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Results achieved show that LOF exhibits the best performance across the different datasets, thus showing better suitability for outlier detection in the context of frameworks that perform PoI detection ...
This paper provides an analysis of two machine learning algorithms, density-based spatial clustering of applications with noise (DBSCAN) and the local outlier factor (LOF), applied in the detection of ...
Privacy Preservation and Security Considerations MCS requires first of all consent by the user. ...
doi:10.3390/fi15010004
fatcat:yip2ujd3dvck5nq5hf7yjw4dxi
Density-Based Outlier Detection for Safeguarding Electronic Patient Record Systems (January 2019)
2019
IEEE Access
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This paper presents a system that employs a density-based local outlier detection model. The system is intended to add to the defense-in-depth of healthcare infrastructures. ...
INDEX TERMS Data analysis, electronic patient records, healthcare infrastructures, information security, patient privacy, visualisation. ...
There is a large volume of literature concerning big-data-based privacy-preserving machine learning algorithms. ...
doi:10.1109/access.2019.2906503
fatcat:7lf3454u4jgitmd3wvdtazeu6e
Oan: aykırı kayıt yönelimli fayda temelli mahremiyet koruma modeli
2019
Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi
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In this study, a new outlier-oriented utility-based privacy preserving model named as OAN, which reduces the computational cost by detecting outliers before anonymization and increases data utility by ...
In this study, a new outlier record-oriented utility-based privacy-preserving model named as OAN, which reduces the computational cost by detecting outliers before anonymization and increases data utility ...
Wang H.W., Liu R., Hiding Outliers into Crowd: Privacy-preserving Data Publishing with Outliers, Data & Knowledge Engineering, 100, 94-115, 2015. 9. ...
doi:10.17341/gazimmfd.467390
fatcat:3k2s5fiqzbcavcamzyuhrlh2cu
Patient Privacy Violation Detection in Healthcare Critical Infrastructures: An Investigation Using Density-Based Benchmarking
2020
Future Internet
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The system outlined in this research employs an internal-focused methodology for anomaly detection by using the Local Outlier Factor (LOF) and Density-Based Spatial Clustering of Applications with Noise ...
Out of 90,385 unique IDs, DBSCAN finds 102 anomalies, whereas 358 are detected using LOF. ...
LOF Outlier Detection Results The LOF visualisation in Figure 8 creates an anomaly priority ordering display, where the advantage of a weighted anomaly score is apparent. ...
doi:10.3390/fi12060100
fatcat:lh46gbt5izbjvogmd5csoe4mz4
Privacy Preserving Nearest Neighbor Search
2006
Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06)
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We show how this algorithm can be used in three important data mining algorithms, namely LOF outlier detection, SNN clustering, and kNN classification. ...
Privacy preserving data mining techniques have been developed to address this issue by providing mechanisms to mine the data while giving certain privacy guarantees. ...
Previous work on privacy preserving outlier detection [49] required finding the number of neighbors closer than a threshold. ...
doi:10.1109/icdmw.2006.133
dblp:conf/icdm/ShaneckKK06
fatcat:wt5bkrfisngcdmdh3qatvbjv74
Privacy-Preserving Anomaly Detection Using Synthetic Data
[chapter]
2020
Lecture Notes in Computer Science
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Besides anonymisation, which becomes difficult to achieve especially with high dimensional data, one approach for privacy-preserving data mining lies in the usage of synthetic data. ...
to a lack of realistic outliers. ...
For a detailed overview on privacy-preserving data publishing methods, see [5] . ...
doi:10.1007/978-3-030-49669-2_11
fatcat:rvd3js72gnd5fnv3xo5o3p6xs4
Semi-supervised Learning Framework for UAV Detection
[article]
2021
arXiv
pre-print
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We developed a local outlier factor model as the UAV detection algorithm using the coefficient variances of the wavelet packets from WiFi and Bluetooth signals. ...
The application of this approach is not limited to UAV detection as it can be extended to the detection of rogue RF devices in an environment. ...
A global outlier detection algorithm might not be able to detect L 1 and L 2 as outliers. Information about how LOF computes the local density of a datapoint can be found in [21] . ...
arXiv:2104.06614v1
fatcat:mzi3lzwqgjgk5ew33zxnnzicim
Arrays of (locality-sensitive) Count Estimators (ACE)
2018
Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18
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Even being a well-studied topic, existing techniques for unsupervised anomaly detection require storing significant amounts of data, which is prohibitive from memory, latency and privacy perspectives, ...
In addition, ACE has appealing privacy properties. ...
Privacy-preserving anomaly detection is a challenging problem in itself [7] . ...
doi:10.1145/3178876.3186056
dblp:conf/www/LuoS18
fatcat:ysyaoli5kjhezimx6jvh5xzb6i
Achieving differential privacy for k-nearest neighbors based outlier detection by data partitioning
[article]
2021
arXiv
pre-print
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When applying outlier detection in settings where data is sensitive, mechanisms which guarantee the privacy of the underlying data are needed. ...
The k-nearest neighbors (k-NN) algorithm is a simple and one of the most effective methods for outlier detection. ...
The existing works, which have proposed solutions for privacy preserving outlier detection either relax the concept of -DP [3, 5, 15] or use simplified definitions of outliers [17, 4, 13] in order ...
arXiv:2104.07938v1
fatcat:srji6zh7lva5dbiwcuzcsqlmwi
Arrays of (locality-sensitive) Count Estimators (ACE): High-Speed Anomaly Detection via Cache Lookups
[article]
2017
arXiv
pre-print
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These tiny 4MB arrays of counts are sufficient for unsupervised anomaly detection. ...
Anomaly detection is one of the frequent and important subroutines deployed in large-scale data processing systems. ...
It turns out that our proposed ACE algorithm has ideal properties for privacy preserving anomaly detection. ...
arXiv:1706.06664v1
fatcat:touxonk5kbf5nlxyfkbo46cjnu
Detection of Malicious DNS-over-HTTPS Traffic: An Anomaly Detection Approach using Autoencoders
[article]
2023
arXiv
pre-print
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We run extensive experiments to evaluate the performance of our proposed autoencoder and compare it to that of other anomaly detection algorithms, namely, local outlier factor, one-class support vector ...
To maintain the privacy of users' web browsing history, popular browsers encrypt their DNS traffic using the DNS-over-HTTPS (DoH) protocol. ...
We summarize our contributions as follows. • We design a privacy-preserving anomaly detection autoencoder to detect malicious DoH traffic. ...
arXiv:2310.11325v1
fatcat:fgophas3mnaerk7cn3445b6olm
Custom Outlier Detection for Electrical Energy Consumption Data Applied in Case of Demand Response in Block of Buildings
2021
Sensors
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Quantitative and qualitative methods were created with the scope of error reduction and were covered in multiple surveys and overviews to cope with outlier detection. ...
The aim of this paper is to provide an extended analysis of the outlier detection, using probabilistic and AI techniques, applied in a demo pilot demand response in blocks of buildings project, based on ...
The adjustment of the valid outliers was conducted using one of the most popular outlier detection techniques in literature, that is the shape-preserving piecewise cubic spline interpolation [65] . ...
doi:10.3390/s21092946
pmid:33922298
fatcat:c6drxafbg5gxhbghwjqm2bsjza
Outlier Detection using AI: A Survey
[article]
2021
arXiv
pre-print
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It is important to detect outlier events as carefully as possible to avoid infrastructure failures because anomalous events can cause minor to severe damage to infrastructure. ...
Accordingly, and due to its variability, Outlier Detection (OD) is an ever-growing research field. In this chapter, we discuss the progress of OD methods using AI techniques. ...
However, most of the real-world datasets are not publicly, because of security and privacy concerns. ...
arXiv:2112.00588v1
fatcat:yonfnhohpnbxxgiwrxon74mcny
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