T. W. Chim, S. M. Yiu, Lucas C. K. Hui and Victor O. K. Li (December 12th 2012). Privacy-Preserving Information Gathering Using VANET, Wireless Ad-Hoc Networks, Hongbo Zhou, IntechOpen, DOI: 10.5772/53537. 

Jaydip Sen (March 14th 2012). Secure and Privacy-Preserving Authentication Protocols for Wireless Mesh Networks, Applied Cryptography and Network Security, Jaydip Sen, IntechOpen, DOI: 10.5772/39176. 

Rima Belguechi, Estelle Cherrier, Vincent Alimi, Patrick Lacharme and Christophe Rosenberger (July 27th 2011). An Overview on Privacy Preserving Biometrics, Recent Application in Biometrics, Jucheng Yang and Norman Poh, IntechOpen, DOI: 10.5772/19338.

J. Indumathi (August 29th 2012). A Generic Scaffold Housing the Innovative Modus Operandi for Selection of the Superlative Anonymisation Technique for Optimized Privacy Preserving Data Mining, Data Mining Applications in Engineering and Medicine, Adem Karahoca, IntechOpen, DOI: 10.5772/49982. 

Malta

The pervasiveness of cameras and associated sensors is offering incredible opportunities to improve services and our quality of life, while however simultaneously posing important societal challenges. Enabling multimedia applications and services while protecting privacy is a major challenge that must be addressed to adapt to increased capabilities of classifiers and changing demands from users. In this talk I will cover new privacy preserving analytical frameworks for single and multimedia sensor data, such as images, videos, audio, as well as accelerometer and gyroscope data. Specifically, I will discuss as applications image sharing in social media, mobile-based health, and video capturing, and talk about desirable (and undesirable) inferences from various types of data.

ICPRAM 2020

Privacy-preserving Machine Learning for Multimedia Data

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
AAAI is providing you with a conference planner, which you can use to help organize your itinerary of activities. This includes talks to attend in person, talks to attend remotely, breaks with colleagues and your site seeing activities. To access this conference planner, please go to [https://aaai-2023.takemobi.io](https://aaai-2023.takemobi.io).

In order to access this site, you need to register. If you haven't already, please register [here](https://aaai.org/Conferences/AAAI-23/registration/).


AAAI 2023

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

technical paper

The International Conference on Pattern Recognition Applications and Methods is a major point of contact between researchers, engineers and practitioners on the areas of Pattern Recognition and Machine Learning, both from theoretical and application perspectives.
Contributions describing applications of Pattern Recognition techniques to real-world problems, interdisciplinary research, experimental and/or theoretical studies yielding new insights that advance Pattern Recognition methods are especially encouraged.

The International Conference on Pattern Recognition Applications and Methods is a major point of contact between researchers, engineers and practitioners on the areas of Pattern Recognition and Machine Learning, both from theoretical and application perspectives.

One of the challenging problems in digital image forensics is the capability to identify images that are captured by the same camera device. This knowledge can help forensic experts in gathering intelligence about suspects by analyzing digital images. In this paper, we propose a two-part network to quantify the likelihood that a given pair of images have the same source camera, and we evaluated it on the benchmark Dresden data set containing 1851 images from 31 different cameras. To the best of our knowledge, we are the first ones addressing the challenge of device-based image matching. Though the proposed approach is not yet forensics ready, our experiments show that this direction is worth pursuing, achieving at this moment 85 percent accuracy. This ongoing work is part of the EU-funded project 4NSEEK concerned with forensics against child sexual abuse.

People-centric, Trustworthy AI: Humans Enhancing AI and AI Enhancing Humans

Deep learning has boosted the performance of many applications tremendously, such as object classification and detection in images, speech recognition and understanding, machine translation, game play such as chess and go etc. However, these all constitute reasonably narrowly and well defined tasks for which it is reasonable to collect very large datasets. For artificial general intelligence (AGI) we will need to learn from a small number of samples, generalize to entirely new domains, and reason about a problem. What do we need in order to make progress to AGI? I will argue that we need to combine the data generating process, such as the physics of the domain and the causal relationships between objects, with the tools of deep learning. In this talk I will present a first attempt to integrate the theory of graphical models, which arguably was the dominating modeling machine learning paradigm around the turn of the twenty-first century, with deep learning. Graphical models express the relations between random variables in an interpretable way, while probabilistic inference in such networks can be used to reason about these variables. We will propose a new hybrid paradigm where probabilistic message passing in such networks is enhanced with graph convolutional neural networks to improve the ability of such systems to reason and make predictions.

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