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Sihem Amer-Yahia


Sihem Amer-Yahia is Research Director at CNRS at LIG in Grenoble where she leads the SLIDE team. Before that, she was Principal Scientist at QCRI, and Senior Scientist at Yahoo! Research and AT&T Labs. Sihem has served on the SIGMOD Executive Board and on the PVLDB and the EDBT Endowments. She is Editor-in-Chief of the VLDB Journal. She is PC chair for VLDB 2018. Sihem received her Ph.D. in Computer Science from INRIA in 1999.

Human Factors in Crowdsourcing

Today, crowdsourcing is used to "taskify" any job ranging from simple receipt transcription to collaborative editing, fan-subbing, citizen science, and citizen journalism. The crowd is typically volatile, its arrival and departure asynchronous, and its levels of attention and accuracy diverse. Tasks vary in complexity and may necessitate the participation of workers with varying degrees of expertise. This uber-ization of human labor requires the understanding of workers' qualifications and motivation in completing a task, their ability to work together in collaborative tasks, as well as, helping workers find relevant tasks. For over 40 years, organization studies have thoroughly examined human factors that affect workers in physical workplaces. More recently, computer scientists have developed algorithms that verify and lever- age those findings in crowdsourcing platforms.

The goal of this tutorial is to review those two areas and discuss how their combination may improve workers' experience, task throughput and outcome quality for both microtasks and collaborative tasks. I will dedicate time to discussing open research questions that result from the proposed review.

Pierangela Samarati


Pierangela Samarati is a Professor at the Department of Computer Science of the Università degli Studi di Milano, Italy. Her main research interests are access control policies, models and systems, data security and privacy, information system security, and information protection in general. She is the project coordinator of the ESCUDO-CLOUD project, funded by the EC H2020 programme, and she has participated in several projects involving different aspects of information protection. On these topics she has published more than 250 peer-reviewed articles in international journals, conference proceedings, and book chapters. She has been Computer Scientist in the Computer Science Laboratory at SRI, CA (USA). She has been a visiting researcher at the Computer Science Department of Stanford University, CA (USA), and at the Center for Secure Information Systems of George Mason University, VA (USA). She is the chair of the IEEE Systems Council Technical Committee on Security and Privacy in Complex Information Systems (TCSPCIS), of the ERCIM Security and Trust Management Working Group (STM), and of the Steering Committees of the European Symposium On Research In Computer Security (ESORICS) and of the ACM Workshop on Privacy in the Electronic Society (WPES). She is member of several steering committees. She is ACM Distinguished Scientist (named 2009) and IEEE Fellow (named 2012). She has received the IEEE Computer Society Technical Achievement Award (2016). She has been awarded the IFIP TC11 Kristian Beckman Award (2008) and the IFIP WG 11.3 Outstanding Research Contributions Award (2012). She has served as General Chair, Program Chair, and program committee member of several international conferences and workshops.

Data Security and Privacy in the Cloud

The "cloud" has become a successful paradigm for conveniently storing, accessing, processing, and sharing information. With its significant benefits of scalability and elasticity, the cloud paradigm has appealed companies and users, which are more and more resorting to the multitude of available cloud providers for storing and processing data. Unfortunately, such a convenience comes at the price of loss of control over these data by their owner and of consequent new security threats that can limit the potential widespread adoption and acceptance of the cloud computing paradigm. In this tutorial, I will discuss security and privacy issues arising in the cloud scenario, addressing problems related to guaranteeing confidentiality and integrity of data stored or processed by external providers.

The interaction in the second case study was initiated by theoreticians, who wanted to lay the foundations for “data exchange”, in which data is converted from one format to another. Although this problem may sound mundane, the issues that arise are fascinating, and this work made data exchange a new subfield, with special sessions in every major database conference.

This talk will be completely self-contained, and the speaker will derive morals from the case studies.