scholarly journals Multilevel Privacy Controlling Scheme to Protect Behavior Pattern in Smart IoT Environment

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Asad Khan ◽  
Muhammad Mehran Arshad Khan ◽  
Muhammad Awais Javeed ◽  
Muhammad Umar Farooq ◽  
Adeel Akram ◽  
...  
Keyword(s):  
Location Privacy ◽  
Pattern Mining ◽  
Behavior Pattern ◽  
Design Model ◽  
Sensor Nodes ◽  
Frequent Pattern ◽  
Time Duration ◽  
Four Levels ◽  
Traditional Approaches ◽  
Privacy Level

Traditional approaches generally focus on the privacy of user’s identity in a smart IoT environment. Privacy of user’s behavior pattern is an important research issue to address smart technology towards improving user’s life. User’s behavior pattern consists of daily living activities in smart IoT environment. Sensor nodes directly interact with activities of user and forward sensing data to service provider server (SPS). While availing the services provided by a server, users may lose privacy since the untrusted devices have information about user’s behavior pattern and it may share data with adversary. In order to resolve this problem, we propose a multilevel privacy controlling scheme (MPCS) which is different from traditional approaches. MPCS is divided into two parts: (i) behavior pattern privacy degree (BehaviorPrivacyDeg), which works as follows: firstly, frequent pattern mining-based time-duration algorithm (FPMTA) finds the normal pattern of activity by adopting unsupervised learning. Secondly, patterns compact algorithm (PCA) is proposed to store and compact the mined pattern in each sensor device. Then, abnormal activity detection time-duration algorithm (AADTA) is used by current triggered sensors, in order to compare the current activity with normal activity by computing similarity among them; (ii) multilevel privacy design model: we have divided privacy of users into four levels in smart IoT environment, and by using these levels, the server can configure privacy level for users according to their concern. Multilevel privacy design model consists of privacy-level configuration protocol (PLCP) and activity design model. PLCP provides fine privacy controls to users while enabling users to set privacy level. In PLCP, we introduce level concern privacy algorithm (LCPA) and location privacy algorithm (LPA), so that adversary could not damage the data of user’s behavior pattern. Experiments are performed to evaluate the accuracy and feasibility of MPCS in both simulation and real-case studies. Results show that our proposed scheme can significantly protect the user’s behavior pattern by detecting abnormality in real time.

An Adaptive Data Distribution Through Tree Rules in Frequent Pattern Mining

2018 ◽  
pp. 300-305
Keyword(s):  
Information Sharing ◽  
Pattern Mining ◽  
Data Distribution ◽  
Frequent Pattern Mining ◽  
Frequent Pattern ◽  
General Development ◽  
Secure Information ◽  
Evaluation Parameters ◽  
Secure Information Sharing

Information sharing among the associations is a general development in a couple of zones like business headway and exhibiting. As bit of the touchy principles that ought to be kept private may be uncovered and such disclosure of delicate examples may impacts the advantages of the association that have the data. Subsequently the standards which are delicate must be secured before sharing the data. In this paper to give secure information sharing delicate guidelines are bothered first which was found by incessant example tree. Here touchy arrangement of principles are bothered by substitution. This kind of substitution diminishes the hazard and increment the utility of the dataset when contrasted with different techniques. Examination is done on certifiable dataset. Results shows that proposed work is better as appear differently in relation to various past strategies on the introduce of evaluation parameters.

Learning and Synchronized Privacy Preserving Frequent Pattern Mining

2011 ◽  
Vol 22 (8) ◽  
pp. 1749-1760
Author(s):  
Yu-Hong GUO ◽  
Yun-Hai TONG ◽  
Shi-Wei TANG ◽  
Leng-Dong WU
Keyword(s):  
Pattern Mining ◽  
Frequent Pattern Mining ◽  
Privacy Preserving ◽  
Frequent Pattern

scholarly journals Genotype Pattern Mining for Pairs of Interacting Variants Underlying Digenic Traits

Genes ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 1160
Author(s):  
Atsuko Okazaki ◽  
Sukanya Horpaopan ◽  
Qingrun Zhang ◽  
Matthew Randesi ◽  
Jurg Ott
Keyword(s):  
Null Hypothesis ◽  
Pattern Mining ◽  
Genetic Diseases ◽  
Frequent Pattern Mining ◽  
Case Control ◽  
Frequent Pattern ◽  
Permutation Testing ◽  
Case Control Studies ◽  
P Values ◽  
Dna Variants

Some genetic diseases (“digenic traits”) are due to the interaction between two DNA variants, which presumably reflects biochemical interactions. For example, certain forms of Retinitis Pigmentosa, a type of blindness, occur in the presence of two mutant variants, one each in the ROM1 and RDS genes, while the occurrence of only one such variant results in a normal phenotype. Detecting variant pairs underlying digenic traits by standard genetic methods is difficult and is downright impossible when individual variants alone have minimal effects. Frequent pattern mining (FPM) methods are known to detect patterns of items. We make use of FPM approaches to find pairs of genotypes (from different variants) that can discriminate between cases and controls. Our method is based on genotype patterns of length two, and permutation testing allows assigning p-values to genotype patterns, where the null hypothesis refers to equal pattern frequencies in cases and controls. We compare different interaction search approaches and their properties on the basis of published datasets. Our implementation of FPM to case-control studies is freely available.

scholarly journals Frequent Pattern Mining Using Db-Scan Algorithm.

2021 ◽  
Vol 1916 (1) ◽  
pp. 012054
Author(s):  
M Kavitha Margret ◽  
A Ponni ◽  
A Priyanka
Keyword(s):  
Pattern Mining ◽  
Frequent Pattern Mining ◽  
Frequent Pattern

Customized frequent patterns mining algorithms for enhanced Top-Rank-K frequent pattern mining

2021 ◽  
Vol 169 ◽  
pp. 114530
Author(s):  
Areej Ahmad Abdelaal ◽  
Sa'ed Abed ◽  
Mohammad Al-Shayeji ◽  
Mohammad Allaho
Keyword(s):  
Pattern Mining ◽  
Frequent Pattern Mining ◽  
Frequent Pattern ◽  
Frequent Patterns ◽  
Mining Algorithms

scholarly journals Frequent Pattern Mining of Eye-Tracking Records Partitioned into Cognitive Chunks

2014 ◽  
Vol 2014 ◽  
pp. 1-8
Author(s):  
Noriyuki Matsuda ◽  
Haruhiko Takeuchi
Keyword(s):  
Eye Tracking ◽  
Pattern Mining ◽  
Frequent Pattern Mining ◽  
Frequent Pattern ◽  
Web Page ◽  
Threshold Levels ◽  
Attentional Shifts

Assuming that scenes would be visually scanned by chunking information, we partitioned fixation sequences of web page viewers into chunks using isolate gaze point(s) as the delimiter. Fixations were coded in terms of the segments in a5×5mesh imposed on the screen. The identified chunks were mostly short, consisting of one or two fixations. These were analyzed with respect to the within- and between-chunk distances in the overall records and the patterns (i.e., subsequences) frequently shared among the records. Although the two types of distances were both dominated by zero- and one-block shifts, the primacy of the modal shifts was less prominent between chunks than within them. The lower primacy was compensated by the longer shifts. The patterns frequently extracted at three threshold levels were mostly simple, consisting of one or two chunks. The patterns revealed interesting properties as to segment differentiation and the directionality of the attentional shifts.

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