1,713 research outputs found
Data privacy in knowledge discovery
No full textThis thesis addresses data privacy in various stages of extracting knowledge embedded in databases. Advances in computer networking and database technologies have enabled the collection and storage of vast quantities of data. Legal and ethical considerations might require measures to protect an individual's privacy in any use or release of the data. In this thesis, we address the problem of preserving privacy in the two following cases: (1) in distributed knowledge discovery; (2) in situations where the output of a data mining algorithm could itself breach privacy. We present results in two different models, namely secure multiparty computation (SMC) and differential privacy. The first part of the thesis presents privacy preserving protocols in the SMC model. Secure multiparty computation involves the collaborative computation of functions based on inputs from multiple parties. The privacy goal is to ensure that all parties receive only the final output without any party learning anything beyond what can be inferred from the output. Within this framework we address the problem of preserving privacy in the preprocessing and the data mining stages of knowledge discovery in databases. For the preprocessing stage, we present private protocols for the imputation of missing data in a dataset that is shared between two parties. For the data mining stage, we introduce the notion of arbitrarily partitioned data that generalizes both horizontally and vertically partitioned data. We present a privacy-preserving protocol for k-means clustering of arbitrarily partitioned data. We also develop a new simple k-clustering algorithm that was designed to be converted into a communication-efficient protocol for private clustering. The second part of the thesis deals with privacy in situations where the output of a data mining algorithm could itself breach privacy. In this setting, we present private inference control protocols in the SMC model for On-line Analytical Processing systems. In the differential privacymodel, the goal is to provide access to a statistical database while preserving the privacy of every individual in the database, irrespective of any auxiliary information that may be available to the database client. Under this privacy model, we present a practical privacy preserving decision tree classifier using random decision trees.Ph.D.Includes abstractVitaIncludes bibliographical referencesby Geetha Jagannatha
Switching Equivalence in Symmetric n-Sigraphs-V
No full textIntroducing a new notion S-antipodal symmetric n-sigraph of a symmetric n-sigraph and its properties are obtained. Also giving the relation between antipodal symmetric n-sigraphs and S-antipodal symmetric n-sigraphs. Further, discussing structural characterization of S-antipodal symmetric n-sigraphs
Salman Rushdie Est Ouest (nouvelles), coll. «Feux croisés», 1997
No full textGanapathy-doré Geetha. Salman Rushdie Est Ouest (nouvelles), coll. «Feux croisés», 1997. In: Hommes et Migrations, n°1207, Mai-juin 1997. Imaginaire colonial, figures de l'immigré. p. 136
Salman Rushdie, La Terre sous ses pieds
No full textGanapathy-doré Geetha. Salman Rushdie, La Terre sous ses pieds. In: Hommes et Migrations, n°1222, Novembre-décembre 1999. Pays-de-la-Loire divers et ouverts. pp. 134-137
Studies of Histidine, Phenylalanine Complexes of Oxovanadium(IV) Derived from Acetylacetone
No full textSchiff base complexes of oxovanadium(IV) with amino acids and acetylacetone were synthesized and characterized by elemental analysis, conductivity measurements, spectral and magnetic data. The complexes were found to be non-electrolytes and stoichiometry shown 1:1. The spectral and magnetic data were suggesting the square pyramidal geometr
Book Review: Praveen Jha and Geetha Rani (eds), Right to Education in India: Resources, Institutions and Public Policy
No full textPraveen Jha and Geetha Rani (eds), Right to Education in India: Resources, Institutions and Public Policy. New Delhi: Routledge, 2016, ₹895 (hardback), 366 pp.</jats:p
Carbon-efficient virtual machine placement based on dynamic voltage frequency scaling in geo-distributed cloud data centers
No full textThe tremendous growth of big data analysis and IoT (Internet of Things) has made cloud computing an integral part of society. The prominent problem associated with data centers is the growing energy consumption, which results in environmental pollution. Data centers can reduce their carbon emissions through efficient management of server power consumption for a given workload. Dynamic voltage frequency scaling (DVFS) can be applied to control the operating frequencies of the servers based on the workloads assigned to them, as this approach has a cubic increment relationship with power consumption. This research work proposes two DVFS-enabled host selection algorithms for virtual machine (VM) placement with a cluster selection strategy, namely the carbon and power-efficient optimal frequency (C-PEF) algorithm and the carbon-aware first-fit optimal frequency (C-FFF) algorithm. The main aims of the proposed algorithms are to balance the load among the servers and dynamically tune the cooling load based on the current workload. The cluster selection strategy is based on static and dynamic power usage effectiveness (PUE) values and the carbon footprint rate (CFR). The cluster selection is also extended to non-DVFS host selection policies, namely the carbon-and power-efficient (C-PE) algorithm, carbon-aware first-fit (C-FF) algorithm, and carbon-aware first-fit least-empty (C-FFLE) algorithm. The results show that C-FFF achieves 2% more power reduction than C-PEF and C-PE, and demonstrates itself as a power-efficient algorithm for CO2 reduction, retaining the same quality of service (QoS) as its counterparts with lower computational overheads
The Line n-Sigraph of a Symmetric n-Sigraph-IV
No full textUnless mentioned or defined otherwise, for all terminology and notion in graph theory the reader is refer to [6]. We consider only finite, simple graphs free from self-loops
A dangerous but powerful idea - counter acceleration and speed with slowness and wholeness
No full textThe dangerous idea is that school reform, in India in particular, but across the world too, is impossible. Changing education, at the systemic level or at the institutional or school level, or educating teachers and school leaders in change can be classified as largely first order change - that of school improvement, which involves doing more of the same but doing it better (where the focus is on efficiency) and that of school re-structuring, which involves re-organising components and responsibilities (where the focus is on effectiveness). Geetha Narayanan is Principal Investigator with Project Vision at the Centre for Education Research Training and Development (CERTAD) within the Srishti School of Art Design and Technology in Bangalore, India. She has dedicated her career to finding and establishing new models of education that are creative, synergistic and original in their approach to learning. Read the article and listen to audio of the author discussing her ideas
AI and blockchain applications in industrial robotics
No full textThe ever-evolving industrial landscape poses challenges for businesses, particularly in the field of robotics. Performance optimization, data security, and harnessing emerging technologies are crucial for staying competitive. Traditional approaches fall short in addressing these complexities, leaving industries searching for innovative solutions.
AI and Blockchain Applications in Industrial Robotics is an exceptional book which presents the transformative potential of combining artificial intelligence (AI) and blockchain technologies to revolutionize industrial robotics.
The book provides comprehensive insights into how AI enhances predictive models and pattern recognition while blockchain ensures secure and immutable data transactions. By synergizing these technologies, businesses can achieve enhanced transparency, trust, and efficiency in their robotic processes. It offers practical applications, use cases, and real-world examples that cater to postgraduate students, researchers, industry professionals, undergraduate students, and freelance developers, empowering them to embrace the possibilities of AI and blockchain in industrial robotics.
With AI and Blockchain Applications in Industrial Robotics, many industries can overcome the challenges they face in optimizing performance, ensuring data security, and embracing the potential of emerging technologies. This book serves as a beacon of knowledge, equipping readers with the tools and understanding to implement AI and blockchain solutions, driving innovation, efficiency, and competitiveness in the industrial sector
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