A Privacy Preservation Model for RFID Data-Collections is Highly Secure and More Efficient than LKC-Privacy

Author(s):  
Surapon Riyana ◽  
Noppamas Riyana
2001 ◽  
Vol 6 (1) ◽  
pp. 26-35 ◽  
Author(s):  
Marja Kokkonen ◽  
Lea Pulkkinen ◽  
Taru Kinnunen

The study was part of the Jyväskylä Longitudinal Study of Personality and Social Development, underway since 1968, in which children's low self-control of emotions was studied using teacher ratings at age 8 in terms of inattentiveness, shifting moods, aggression, and anxiety. The study was based on data from 112 women and 112 men who participated in the previous data collections at ages 8, 27, and 36. At age 27, the participants had been assessed in Neuroticism (N) using the Eysenck Personality Questionnaire , and at age 36 they filled in several inventories measuring, among others, conscious and active attempts to repair negative emotions in a more positive direction as well as physical symptoms. The present study used structural equation modeling to test the hypothesis that personality characteristics indicating low self-control of emotions at ages 8 and 27 are antecedents of self-reported physical symptoms at age 36; and that this relationship is indirect, mediated by attempts to repair negative emotions in a more positive direction. The findings showed, albeit for men only, that inattentiveness at age 8 was positively related to self-reported physical symptoms at age 36 via high N at age 27 and low attempts to repair negative emotions at age 36. Additionally, N at age 27 was directly linked to self-reported physical symptoms at age 36. The mediation of an active attempt to repair negative emotions was not found for women. Correlations revealed, however, that shifting moods and aggression in girls were antecedents of self-reported physical symptoms in adulthood, particularly, pain and fatigue.


Author(s):  
Renita Prera Winsen

பேராக் மாநிலத்தில் தைப்பிங் மாவட்டத்தில் அமைந்துள்ள ஓர் இடைநிலைப்பள்ளியில் திருக்குறள் கற்றலின் வழி படிவம் 2 மாணவர்களின் உயர்நிலைச் சிந்தனைத் திறனை மேம்படுத்தும் முயற்சியில் ஆய்வு மேற்கொள்ளப்பட்டது. தேர்ந்தெடுக்கப்பட்ட 10 மாணவர்கள் இந்த ஆய்வில் உட்படுத்தப்பட்டனர். திருக்குறளில் மாணவர்களின் ஆளுமையைக் கண்டறிய அந்த இடைநிலைப்பள்ளியின் தமிழாசிரியரிடம் நேர்காணல் நடத்தப்பட்டது. மாணவர்களின் உயர்நிலைச் சிந்தனைத் திறனை மேம்படுத்த படிவம் 1 மற்றும் படிவம் 2-இல் வரையறுக்கப்பட்ட ஆறு திருக்குறள்கள் தேர்தெடுக்கப்பட்டன. தேர்ந்தெடுக்கப்பட்ட திருக்குறள்கள் யாவும் சீரமைக்கப்பட்ட புளூமின் அறிவுசார் முறைப்பாட்டியலின் துணைக்கொண்டு பலதரப்பட கேள்விகள் தயாரிக்கப்பட்டது. ஆறு வாரத் திருக்குறள் வகுப்பிற்குப் பின் இக்கேள்விகள் யாவும் மாணவர்களுக்கு வழங்கப்பட்டன. கேள்விக்கான பதில்களிலிருந்து மாணவர்களின் உயர்நிலைச் சிந்தனைத் திறனில் ஏற்பட்ட மாற்றங்கள் கண்டறியப்பட்டது. ஆய்வின் முடிவாக, முறையான திருக்குறள் கற்றலின் வழி மாணவர்களின் உயர்நிலைச் சிந்தனைத் திறனை மேம்படுத்த முடியும் என்பது உறுதிச் செய்யப்பட்டது. (This study has been conducted with the purpose of improving the level of HOTS (Higher order thinking skills) of Form 2 students through learning Thirukkural. For this study, the Thirukkural, a well-known literary work of Tamil Language was taken. Thus, this research was carried out in a secondary school which is located at Taiping, Perak. The research was carried out under the design of action research. The sample of this study consisted of ten Form 2 students. Besides that, a teacher also interviewed in order to know the students' personality in learning Thirukkural. In this research, the learning process of Thirukkural approach was implemented for 6 weeks. There are 6 couplets of Thirukkural selected according to the syllabus of Form 1 and Form 2. This six couplets of Thirukkural used to test the level of HOTS. The questions were created based on Thirukkural, according to Revised Bloom's Taxonomy. The data of the study was collected through pre-test, the questions asked in Thirukkural classes and post-test via qualitative and quantitative data collection tools. The findings obtained through qualitative and quantitative data collections showed that the level of HOTS through learning Thirukkural among Form 2 students has improved.)


2019 ◽  
Vol 7 (10) ◽  
pp. 185-190
Author(s):  
Sapna Bhardwaj ◽  
Sagun Sharma ◽  
Anuradha .
Keyword(s):  

2010 ◽  
Vol 21 (4) ◽  
pp. 632-643 ◽  
Author(s):  
Yu GU ◽  
Ge YU ◽  
Xiao-Long HU ◽  
Yi WANG
Keyword(s):  

2009 ◽  
Vol 29 (10) ◽  
pp. 2786-2790 ◽  
Author(s):  
Xiao-jia YIN ◽  
Shi-guang JU ◽  
Ying-jie WANG

Author(s):  
Shalin Eliabeth S. ◽  
Sarju S.

Big data privacy preservation is one of the most disturbed issues in current industry. Sometimes the data privacy problems never identified when input data is published on cloud environment. Data privacy preservation in hadoop deals in hiding and publishing input dataset to the distributed environment. In this paper investigate the problem of big data anonymization for privacy preservation from the perspectives of scalability and time factor etc. At present, many cloud applications with big data anonymization faces the same kind of problems. For recovering this kind of problems, here introduced a data anonymization algorithm called Two Phase Top-Down Specialization (TPTDS) algorithm that is implemented in hadoop. For the data anonymization-45,222 records of adults information with 15 attribute values was taken as the input big data. With the help of multidimensional anonymization in map reduce framework, here implemented proposed Two-Phase Top-Down Specialization anonymization algorithm in hadoop and it will increases the efficiency on the big data processing system. By conducting experiment in both one dimensional and multidimensional map reduce framework with Two Phase Top-Down Specialization algorithm on hadoop, the better result shown in multidimensional anonymization on input adult dataset. Data sets is generalized in a top-down manner and the better result was shown in multidimensional map reduce framework by the better IGPL values generated by the algorithm. The anonymization was performed with specialization operation on taxonomy tree. The experiment shows that the solutions improves the IGPL values, anonymity parameter and decreases the execution time of big data privacy preservation by compared to the existing algorithm. This experimental result will leads to great application to the distributed environment.


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