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Harokopio University, Greece

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7 Papers

Partially Oblivious Neural Network Inference

Topics: Applied Cryptography; Privacy Enhancing Technologies; Machine Learning and AI Security

Authors: Panagiotis Rizomiliotis , Christos Diou , Aikaterini Triakosia , Ilias Kyrannas and Konstantinos Tserpes

Affiliation: Dep. of Informatics and Telematics, Harokopio University, Omirou 9, Athens, Greece

Digital Object Identifier: 10.5220/0011272500003283

Proceedings of SECRYPT 2022, ISBN: 978-989-758-590-6

Quick Abstract

Oblivious inference is the task of outsourcing a ML model, like neural-networks, without disclosing critical and sensitive information, like the model’s parameters. One of the most prominent solutions for secure oblivious inference is based on a powerful cryptographic tools, like Homomorphic Encryption (HE) and/or multi-party computation (MPC). Even though the implementation of oblivious inference systems schemes has impressively improved the last decade, there are still significant limitations on the ML models that they can practically implement. Especially when both the ML model and the input data’s confidentiality must be protected. In this paper, we introduce the notion of partially oblivious inference. We empirically show that for neural network models, like CNNs, some information leakage can be acceptable. We therefore propose a novel trade-off between security and efficiency. In our research, we investigate the impact on security and inference runtime performance from the CNN model’s weights partial leakage. We experimentally demonstrate that in a CIFAR-10 network we can leak up to 80% of the model’s weights with practically no security impact, while the necessary HE-mutliplications are performed four times faster.

“I Want to ... Change”: Micro-moment based Recommendations can Change Users’ Energy Habits

Topics: Smart Homes (Domotics); Optimization Techniques for Efficient Energy Consumption; User-Centred and Participatory Design of Services and Systems for Smart Cities; Smart Sensor-Based Networks and Applications; Case Studies, Business Models and Innovative Applications for Smart(er) Cities

Authors: Christos Sardianos ¹ , Iraklis Varlamis ¹ , George Dimitrakopoulos ¹ , Dimosthenis Anagnostopoulos ¹ , Abdullah Alsalemi ² , Faycal Bensaali ² and Abbes Amira ³

Affiliations: ¹ Department of Informatics and Telematics, Harokopio University of Athens, Athens and Greece , ² Department of Electrical Engineering, Qatar University, Doha and Qatar , ³ Department of Computer Science and Engineering, Qatar University, Doha and Qatar

Digital Object Identifier: 10.5220/0007673600300039

Proceedings of SMARTGREENS 2019, ISBN: 978-989-758-373-5

Quick Abstract

Since electricity consumption of households in developing countries is dramatically increasing every year, it is now more prudent than ever to utilize technology-based solutions that assist energy end-users to improve energy efficiency without affecting quality of life. User behavior is the most important factor that influences household energy consumption and recommender systems can be the technology enabler for shaping the users’ behavior towards energy efficiency. The current literature mostly focuses on energy usage monitoring and home automation and fails to engage and motivate users, who are not as committed and self-motivated. In this work, we present a context-aware recommender system that analyses user activities and understands their habits. Based on the output of this analysis, the system synchronizes with the user activities and presents personalized energy efficiency recommendations at the right moment and place. The recommendation algorithm considers user preferences, energy goals, and availability in order to maximize the acceptance of a recommended action and increase the efficiency of the recommender system. The results from the evaluation on a publicly available dataset comprising energy consumption data from multiple devices shows that micro-moments repeatedly occur within user’s timeline (covering more than 35% of user future activities) and can be learned from user logs.

Related Ontology Subjects/Areas/Topics (8)

Energy and Economy	Smart Cities	Smart Sensor-Based Networks and Applications
Energy-Aware Systems and Technologies	Smart Grids	User-Centred and Participatory Design of Services and Systems for Smart Cities
Optimization Techniques for Efficient Energy Consumption	Smart Homes (Domotics)

BUDaMaF - Data Management in Cloud Federations

Topics: Edge Cloud Data Management; Resource Management; Federated Cloud; Cloud Middleware Frameworks; Cloud Optimization and Automation

Authors: Evangelos Psomakelis ¹ , Konstantinos Tserpes ¹ , Dimosthenis Anagnostopoulos ² and Theodora Varvarigou ³

Affiliations: ¹ Harokopio University of Athens and National Technical University of Athens, Greece , ² Harokopio University of Athens, Greece , ³ National Technical University of Athens, Greece

Digital Object Identifier: 10.5220/0006714704560464

Proceedings of CLOSER 2018, ISBN: 978-989-758-295-0

Quick Abstract

Data management involves quality of service, security, resource management, cost management, incident identification, disaster avoidance and/or recovery, as well as many other concerns. This situation gets ever more complicated because of the divergent nature of cloud federations. The BASMATI Unified Data Management Framework (BUDaMaF) creates an automated uniform way of managing all data transactions, as well as the data stores themselves, in a polyglot multi-cloud consisting of a plethora of different machines and data store systems. It provides a context independent platform providing automated scaling and data migration, tackling in real time disaster scenarios, like sudden usage spikes.

Big IoT and Social Networking Data for Smart Cities - Algorithmic Improvements on Big Data Analysis in the Context of RADICAL City Applications

Authors: Evangelos Psomakelis ¹ , Fotis Aisopos ² , Antonios Litke ² , Konstantinos Tserpes ³ , Magdalini Kardara ² and Pablo Martínez Campo ⁴

Affiliations: ¹ National Technical University of Athens and Harokopio University of Athens, Greece , ² National Technical University of Athens, Greece , ³ Harokopio University of Athens and National Technical University of Athens, Greece , ⁴ University of Cantabria, Spain

Digital Object Identifier: 10.5220/0005934503960405

Proceedings of DataDiversityConvergence 2016/CLOSER, ISBN: 978-989-758-182-3

Quick Abstract

In this paper we present a SOA (Service Oriented Architecture)-based platform, enabling the retrieval and analysis of big datasets stemming from social networking (SN) sites and Internet of Things (IoT) devices, collected by smart city applications and socially-aware data aggregation services. A large set of city applications in the areas of Participating Urbanism, Augmented Reality and Sound-Mapping throughout participating cities is being applied, resulting into produced sets of millions of user-generated events and online SN reports fed into the RADICAL platform. Moreover, we study the application of data analytics such as sentiment analysis to the combined IoT and SN data saved into an SQL database, further investigating algorithmic and configurations to minimize delays in dataset processing and results retrieval.

A Hedonic Price Index for Cloud Computing Services

Topics: Service Marketing and Management; Cloud Economics; Metering, Pricing, and Software Licensing

Authors: Persefoni Mitropoulou , Evangelia FIliopoulou , Stavroula Tsaroucha , Christos Michalakelis and Mara Nikolaidou

Affiliation: Harokopio University of Athens, Greece

Digital Object Identifier: 10.5220/0005410604990505

Proceedings of CLOSER 2015, ISBN: 978-989-758-104-5

Quick Abstract

Cloud computing is an innovative business model, being developed at a fast pace during the last years, offering many operational and economic benefits to both the demand and the supply side of the ICT market. Infrastructure as a Service (IaaS), which includes control of fundamental computing resources, is expected to be the fastest growing model of public cloud computing. Due to the existence of several IaaS cloud providers, there is increased competition among cloud companies, which develop different pricing models in order to meet the market demand. As a consequence, prices for cloud services are a result of a multidimensional function, shaped by the service’s characteristics. The development of a suitable pricing method, based on an appropriate price index able to capture the market dynamics, is an obvious necessity. The aim of this paper is the construction of such a price index, for the IaaS model, using data from a wide range of cloud providers and a large number of price bundles. The hedonic pricing method is used to decompose cloud computing services into their constituent characteristics, obtaining estimates of the contributory value of each resource. According to the results, RAM size, CPU power and subscription turned out to be the most influential factors that shape IaaS pricing.

Related Ontology Subjects/Areas/Topics (7)

Cloud Computing	Metering, Pricing, and Software Licensing	Service Marketing and Management
Cloud Computing Enabling Technology	Platforms and Applications	Services Science
Cloud Economics

Comparing Methods for Twitter Sentiment Analysis

Topics: Machine Learning; Data Analytics; Information Extraction

Authors: Evangelos Psomakelis ¹ , Konstantinos Tserpes ¹ , Dimosthenis Anagnostopoulos ¹ and Theodora Varvarigou ²

Affiliations: ¹ Harokopio University of Athens, Greece , ² National Technical University of Athens, Greece

Digital Object Identifier: 10.5220/0005075302250232

Proceedings of KDIR 2014/IC3K, ISBN: 978-989-758-048-2

Quick Abstract

This work extends the set of works which deal with the popular problem of sentiment analysis in Twitter. It investigates the most popular document ("tweet") representation methods which feed sentiment evaluation mechanisms. In particular, we study the bag-of-words, n-grams and n-gram graphs approaches and for each of them we evaluate the performance of a lexicon-based and 7 learning-based classification algorithms (namely SVM, Naïve Bayesian Networks, Logistic Regression, Multilayer Perceptrons, Best-First Trees, Functional Trees and C4.5) as well as their combinations, using a set of 4451 manually annotated tweets. The results demonstrate the superiority of learning-based methods and in particular of n-gram graphs approaches for predicting the sentiment of tweets. They also show that the combinatory approach has impressive effects on n-grams, raising the confidence up to 83.15% on the 5-Grams, using majority vote and a balanced dataset (equal number of positive, negative and neutral tweets for training). In the n-gram graph cases the improvement was small to none, reaching 94.52% on the 4-gram graphs, using Orthodromic distance and a threshold of 0.001.

Related Ontology Subjects/Areas/Topics (12)

Artificial Intelligence	Data Engineering	Knowledge-Based Systems
Business Analytics	Evolutionary Computing	Machine Learning
Computational Intelligence	Information Extraction	Soft Computing
Data Analytics	Knowledge Discovery and Information Retrieval	Symbolic Systems

INTRODUCING A UML PROFILE FOR DISTRIBUTED SYSTEM CONFIGURATION

Topics: Modeling Formalisms, Languages and Notations; Modeling of Distributed Systems

Authors: N. Alexopoulou ¹ , A. Tsadimas ² , M. Nikolaidou ² , A. Dais ¹ and D. Anagnostopoulos ²

Affiliations: ¹ Harokopio University of Athens; University of Athens, Greece , ² Harokopio University of Athens, Greece

Digital Object Identifier: 10.5220/0002458405420545

Proceedings of ICEIS 2006, ISBN: 978-972-8865-43-6

Quick Abstract

Distributed system configuration consists of distributed application component placement and underlying network design, thus is a complex process dealing with interrelated issues and comprising various stages. A common metamodel for distributed system representation in all configuration stages is thus required, so that unclear dependencies between discrete stages can be easily identified. This model should also be easily adopted by autonomous software tools used for the automation of discrete configuration stages and for the efficient development of system specifications by designers. We propose such a metamodel using UML 2.0. More specifically, we introduce a UML 2.0 profile facilitating distributed system configuration process. In this profile, different UML 2.0 diagrams are integrated and properly extended, in order to model all aspects of the distributed system configuration process.

Related Ontology Subjects/Areas/Topics (4)

Enterprise Information Systems		Modeling Formalisms, Languages and Notations		Modeling of Distributed Systems
Information Systems Analysis and Specification

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