Detecting targeted data poisoning attacks on deep neural networks

Deep neural networks (DNNs) are widely used for various facial image-recognition purposes, including facial recognition and subsequent authentication, and the detection of altered facial images. Unfortunately, due to their widespread use, there have been many works that focus on attacking such DNN-based systems for nefarious purposes. One type of attack on DNNs is called a "targeted data poisoning" attack, which has the goal of injecting photos into the DNNs training set in such a way as to cause the DNN to learn malicious behavior. In the context of facial authentication, this could correspond to unauthorized users gaining access to a target's account, whereas, in deepfake detection, this could translate to causing the DNN to fail to identify when a target's face is the subject of a deepfake image. This report describes targeted data poisoning attacks and proposed defenses on DNN-based systems for facial authentication and deepfake detection, each achieving high accuracy ([greater than] 95 percent) in most cases.Includes bibliographical references

Adversarial Learning of Privacy-Preserving and Task-Oriented Representations

Data privacy has emerged as an important issue as data-driven deep learning has been an essential component of modern machine learning systems. For instance, there could be a potential privacy risk of machine learning systems via the model inversion attack, whose goal is to reconstruct the input data from the latent representation of deep networks. Our work aims at learning a privacy-preserving and task-oriented representation to defend against such model inversion attacks. Specifically, we propose an adversarial reconstruction learning framework that prevents the latent representations decoded into original input data. By simulating the expected behavior of adversary, our framework is realized by minimizing the negative pixel reconstruction loss or the negative feature reconstruction (i.e., perceptual distance) loss. We validate the proposed method on face attribute prediction, showing that our method allows protecting visual privacy with a small decrease in utility performance. In addition, we show the utility-privacy trade-off with different choices of hyperparameter for negative perceptual distance loss at training, allowing service providers to determine the right level of privacy-protection with a certain utility performance. Moreover, we provide an extensive study with different selections of features, tasks, and the data to further analyze their influence on privacy protection

Understanding Compressive Adversarial Privacy

Designing a data sharing mechanism without sacrificing too much privacy can be considered as a game between data holders and malicious attackers. This paper describes a compressive adversarial privacy framework that captures the trade-off between the data privacy and utility. We characterize the optimal data releasing mechanism through convex optimization when assuming that both the data holder and attacker can only modify the data using linear transformations. We then build a more realistic data releasing mechanism that can rely on a nonlinear compression model while the attacker uses a neural network. We demonstrate in a series of empirical applications that this framework, consisting of compressive adversarial privacy, can preserve sensitive information

Preserving Users’ Location Privacy in Mobile Platforms

Mobile and interconnected devices both have witnessed rapid advancements in computing and networking capabilities due to the emergence of Internet-of-Things, Connected Societies, Smart Cities and other similar paradigms. Compared to traditional personal computers, these devices represent moving gateways that offer possibilities to influence new businesses and, at the same time, have the potential to exchange users’ sensitive data. As a result, this raises substantial threats to the security and privacy of users that must be considered. With the focus on location data, this thesis proposes an efficient and socially-acceptable solution to preserve users’ location privacy, maintaining the quality of service, and respecting the usability by not relying on changes to the mobile app ecosystem. This thesis first analyses the current mobile app ecosystem as to apply a privacy-bydesign approach to location privacy from the data computation to its visualisation. From our analysis, a 3-Layer Classification model is proposed that depicts the state-ofthe- art in three layers providing a new perspective towards privacy-preserving locationbased applications. Secondly, we propose a theoretically sound privacy-enhancing model, called LP-Cache, that forces the mobile app ecosystem to make location data usage patterns explicit and maintains the balance between location privacy and service utility. LP-Cache defines two location privacy preserving algorithms: on-device location calculation and personalised permissions. The former incorporates caching technique to determine the location of client devices by means of wireless access points and achieve data minimisation in the current process. With the later, users can manage each app and private place distinctly to mitigate fundamental location privacy threats, such as tracking, profiling, and identification. Finally, PL-Protector, implements LP-Cache as a middleware on Android platform. We evaluate PL-Protector in terms of performance, privacy, and security. Experimental results demonstrate acceptable delay and storage overheads, which are within practical limits. Hence, we claim that our approach is a practical, secure and efficient solution to preserve location privacy in the current mobile app ecosystem

towards adaptive residential buildings traditional and contemporary scenarios in bioclimatic design the case of aleppo

Abstract Traditional architectural typologies could play a crucial role in the environmental architectural contemporary framework, due to many attempts developed in last decades to adopt passive house model and bioclimatic criteria in the Mediterranean areas. According to climate responsive approach, the interactive and adaptive relationship between building, site, and climate consider a basic rule to reduce the environmental impact and improving energy efficiency in buildings. In recent decades this concept has extended to the preservation of the cultural identity of the places. High level of adaptive, sustainable and functional performances could be deduced from the traditional residential buildings as the case of Aleppo proves. The traditional Arab house in Aleppo is based on series of adaptive and sustainable-oriented principles derived from the integration of active and interactive design approaches. The old city of Aleppo (included in the UNESCO List of World Heritage Sites) is considered one of the largest historical cities in the world, in terms of its population number (110,000 people before the war). The damage to cultural and historical heritage by the war asserts the peculiarity of the city in the Eastern Mediterranean area. This paper presents the study that the authors are carrying out on Aleppo, considering the bioclimatic approach as a key element to reorient the future construction process of the Syrian city to achieve the objectives of global sustainability and identify design criteria's for the development of the residential buildings. The study also aims to analyse the mutations which appeared through evolution process of residential buildings and identify the invariant elements and the main trajectories of modification established in the past, confirming their compatibility with the future development of Aleppo

Internet of Things Based Technology for Smart Home System: A Generic Framework

Internet of Things (IoT) is a technology which enables computing devices, physical and virtual objects/devices to be connected to the internet so that users can control and monitor devices. The IoT offers huge potential for development of various applications namely: e-governance, environmental monitoring, military applications, infrastructure management, industrial applications, energy management, healthcare monitoring, home automation and transport systems. In this paper, the brief overview of existing frameworks for development of IoT applications, techniques to develop smart home applications using existing IoT frameworks, and a new generic framework for the development of IoTbasedsmart home system is presented. The proposed generic framework comprises various modules such as Auto-Configuration and Management, Communication Protocol, Auto-Monitoring and Control, and Objects Access Control. The architecture of the new generic framework and the functionality of various modules in the framework are also presented. The proposed generic framework is helpful for making every house as smart house to increase the comfort of inhabitants. Each of the components of generic framework is robust in nature in providing services at any time. The components of smart home system are designed to take care of various issues such as scalability, interoperability, device adaptability, security and privacy. The proposed generic framework is designed to work on all vendor boards and variants of Linux and Windows operating system

Multi-Dimensional-Personalization in mobile contexts

During the dot com era the word "personalisation” was a hot buzzword. With the fall of the dot com companies the topic has lost momentum. As the killer application for UMTS or the mobile internet has yet to be identified, the concept of Multi-Dimensional-Personalisation (MDP) could be a candidate. Using this approach, a recommendation of mobile advertisement or marketing (i.e., recommendations or notifications), online content, as well as offline events, can be offered to the user based on their known interests and current location. Instead of having to request or pull this information, the new service concept would proactively provide the information and services – with the consequence that the right information or service could therefore be offered at the right place, at the right time. The growing availability of "Location-based Services“ for mobile phones is a new target for the use of personalisation. "Location-based Services“ are information, for example, about restaurants, hotels or shopping malls with offers which are in close range / short distance to the user. The lack of acceptance for such services in the past is based on the fact that early implementations required the user to pull the information from the service provider. A more promising approach is to actively push information to the user. This information must be from interest to the user and has to reach the user at the right time and at the right place. This raises new requirements on personalisation which will go far beyond present requirements. It will reach out from personalisation based only on the interest of the user. Besides the interest, the enhanced personalisation has to cover the location and movement patterns, the usage and the past, present and future schedule of the user. This new personalisation paradigm has to protect the user’s privacy so that an approach supporting anonymous recommendations through an extended "Chinese Wall“ will be described