Behavioural verification: preventing report fraud in decentralized advert distribution systems

Service commissions, which are claimed by Ad-Networks and Publishers, are susceptible to forgery as non-human operators are able to artificially create fictitious traffic on digital platforms for the purpose of committing financial fraud. This places a significant strain on Advertisers who have no effective means of differentiating fabricated Ad-Reports from those which correspond to real consumer activity. To address this problem, we contribute an advert reporting system which utilizes opportunistic networking and a blockchain-inspired construction in order to identify authentic Ad-Reports by determining whether they were composed by honest or dishonest users. What constitutes a user's honesty for our system is the manner in which they access adverts on their mobile device. Dishonest users submit multiple reports over a short period of time while honest users behave as consumers who view adverts at a balanced pace while engaging in typical social activities such as purchasing goods online, moving through space and interacting with other users. We argue that it is hard for dishonest users to fake honest behaviour and we exploit the behavioural patterns of users in order to classify Ad-Reports as real or fabricated. By determining the honesty of the user who submitted a particular report, our system offers a more secure reward-claiming model which protects against fraud while still preserving the user's anonymity

Private and secure distribution of targeted advertisements to mobile phones

Online Behavioural Advertising (OBA) enables promotion companies to effectively target users with ads that best satisfy their purchasing needs. This is highly beneficial for both vendors and publishers who are the owners of the advertising platforms, such as websites and app developers, but at the same time creates a serious privacy threat for users who expose their consumer interests. In this paper, we categorize the available ad-distribution methods and identify their limitations in terms of security, privacy, targeting effectiveness and practicality. We contribute our own system, which utilizes opportunistic networking in order to distribute targeted adverts within a social network. We improve upon previous work by eliminating the need for trust among the users (network nodes) while at the same time achieving low memory and bandwidth overhead, which are inherent problems of many opportunistic networks. Our protocol accomplishes this by identifying similarities between the consumer interests of users and then allows them to share access to the same adverts, which need to be downloaded only once. Although the same ads may be viewed by multiple users, privacy is preserved as the users do not learn each other's advertising interests. An additional contribution is that malicious users cannot alter the ads in order to spread malicious content, and also, they cannot launch impersonation attacks

An open framework for flexible plug-in privacy mechanisms in crowdsensing applications

Preserving user privacy is crucial for the wide adoption of crowdsensing and participatory sensing applications that rely on personal devices. Currently, each application comes with its own hardwired and possibly undocumented privacy support (if any), while the horizontal protection mechanisms provided by operating and runtime systems operate at a low level that can significantly harm application utility, or even render an application useless. To achieve greater flexibility, we propose a framework that decouples the privacy mechanism from the application logic so that it can be developed by another, perhaps more trusted party, and which allows the dynamic binding of different privacy mechanisms to the same application running on the user's mobile device. We describe a proof-of-concept implementation of the proposed framework for Android, where privacy mechanisms are independently developed as separate plug-in components. Based on a simple but powerful API, it is possible to implement a wide range of standard privacy approaches, including collaborative schemes that involve data exchanges among multiple personal devices

Συστηματική ανασκόπηση κλινικής και οικονομικής επιβάρυνσης των βακτηριαιμιών που σχετίζονται με τους κεντρικούς καθετήρες παγκοσμίως: Μελέτη περίπτωσης Ελληνικών Μ.Ε.Θ

Σκοπός της μελέτης είναι η ανάδειξη του κοινωνικοοικονομικού αντίκτυπου από την επίπτωση των CRBSI’s/CLABSI’s. Πρόκειται για μια συστηματική επισκόπηση της βιβλιογραφίας η οποία αναλύει την κλινική και οικονομική επιβάρυνση των CRBSI’s/CLABSI’s παγκοσμίως και παρουσιάζει νέες οικονομικά αποδοτικότερες ιατρικές τεχνολογίες με τη βοήθεια οικονομοτεχνικών μοντέλων. Συνολικά και στις δυο μηχανές αναζήτησης βρέθηκαν 225 άρθρα μετά από αναζήτηση με λέξεις κλειδιά central line associated bloodstream infection OR catheter related bloodstream infection AND economic AND cost τη χρονική περίοδο 2010-2018 και αναφέρονταν σε κλινικές δοκιμές είτε μελέτες ασθενών μαρτύρων. Συνολικά 17 μελέτες πληρούσαν τα κριτήρια ένταξης της μελέτης. Βρέθηκε πως το μέσο κόστος θεραπείας ασθενών με CRBSI’s/CLABSI’s παγκοσμίως κυμαίνεται από 11.200€ - 87.928€. Οι CRBSI’s/CLABSI’s σχετίζονται με μεγαλύτερη αύξηση του κινδύνου θνησιμότητας κατά 2.27 φορές και με αύξηση της διάρκειας νοσηλείας έως και 19,6 ημέρες. Πρόγραμμα ποιότητας και πρόληψης λοιμώξεων όπως το ICU Keystone project στις ΗΠΑ βρέθηκε ότι μείωνε κατά 50% τις βακτηριαιμίες και κατά 25% τις μηχανικές επιπλοκές. Συμπερασματικά η πρόληψη λοιμώξεων όπως οι CRBSI’s/CLABSI’s μπορεί να γίνει πιο αποτελεσματικά μέσω της επένδυσης σε νέες ιατρικές τεχνολογίες όπως τα αντιμικροβιακά επιθέματα με χλωρεξιδίνη σε μορφή γέλης είτε σε μορφή σπόγγου. Η εισαγωγή αυτών στην κλινική πράξη θα μείωνε την επίπτωση των επεισοδίων, την διάρκεια νοσηλείας και θα επέφερε μια σημαντική εξοικονόμηση κόστους τόσο στα νοσοκομεία όσο και στα συστήματα υγείας καθιστώντας τα βιώσιμα. Το προσθετό κόστος των νέων τεχνολογιών μπορεί να απορροφηθεί από την εφαρμογή νέων μοντέλων φροντίδας ασθενών που προσδίδουν αξία στην θεραπεία (value-based care) καθώς παρέχουν αμοιβαία οφέλη τόσο στους πληρωτές (ασφάλειες, κυβέρνηση) όσο και στους παρόχους υγείας (νοσοκομεία) επιτρέποντας τους να δουν τα οφέλη μιας νέας θεραπείας ή τεχνολογίας και μετά να την αποζημιώσουν.The purpose of the study is to highlight the socio-economic impact (burden of disease) of CRBSI’s / CLABSI's. This is a systematic literature review that analyzes the clinical and financial burden of CRBSI’s / CLABSI’s worldwide and presents new, more cost-effective medical technologies with the help of econometric models. Altogether, in both search engines, 225 articles were identified after a keyword survey ‘’central line associated bloodstream infection OR catheter related bloodstream infection AND economic AND cost’’ during the 2010-2018 period and reported in clinical trials or patient control studies. A total of 17 studies met the study inclusion criteria. It was found that the average cost of treatment of patients with CRBSI's / CLABSI's worldwide ranges from 11,200 € - 87,928 €. CRBSI's / CLABSI's are associated with a greater increase in mortality risk by 2.27 times and an increase in length of stay up to 19.6 days. A quality and infection prevention program such as the ICU Keystone project in the USA have been found to reduce incidence by 50% and mechanical complications by 25%. In conclusion, the prevention of infections such as CRBSI's / CLABSI's can be made more effective by investing in new medical technologies such as antimicrobial I.V dressings with chlorhexidine in gel or sponge form. Introducing these in the clinical practice would reduce the incidence of episodes, length of hospitalization, and result in significant cost savings in both hospitals and health systems making them sustainable. The added cost of new technologies can be absorbed by the introduction of new value-based care models as they provide mutual benefits to both payers (insurance, government) and healthcare providers (hospitals) by allowing them to see the benefits of a new treatment or technology and then compensate for it

A flexible n/2 adversary node resistant and halting recoverable blockchain sharding protocol

Blockchain sharding is a promising approach to solving the dilemma between decentralisation and high performance (transaction throughput) for blockchain. The main challenge of Blockchain sharding systems is how to reach a decision on a statement among a sub-group (shard) of people while ensuring the whole population recognises this statement. Namely, the challenge is to prevent an adversary who does not have the majority of nodes globally but have the majority of nodes inside a shard. Most Blockchain sharding approaches can only reach a correct consensus inside a shard with at most $n/3$ evil nodes in a $n$ node system. There is a blockchain sharding approach which can prevent an incorrect decision to be reached when the adversary does not have $n/2$ nodes globally. However, the system can be stopped from reaching consensus (become deadlocked) if the adversary controls a smaller number of nodes. In this paper, we present an improved Blockchain sharding approach that can withstand $n/2$ adversarial nodes and recover from deadlocks. The recovery is made by dynamically adjusting the number of shards and the shard size. A performance analysis suggests our approach has a high performance (transaction throughput) while requiring little bandwidth for synchronisation

Ensuring compliance of IoT devices with their Privacy Policy Agreement

In the past few years, Internet of Things (IoT) devices have emerged and spread everywhere. Many researchers have been motivated to study the security issues of IoT devices due to the sensitive information they carry about their owners. Privacy is not simply about encryption and access authorization, but also about what kind of information is transmitted, how it used and to whom it will be shared with. Thus, IoT manufacturers should be compelled to issue Privacy Policy Agreements for their respective devices as well as ensure that the actual behavior of the IoT device complies with the issued privacy policy. In this paper, we implement a test bed for ensuring compliance of Internet of Things data disclosure to the corresponding privacy policy. The fundamental approach used in the test bed is to capture the data traffic between the IoT device and the cloud, between the IoT device and its application on the smart-phone, and between the IoT application and the cloud and analyze those packets for various features. We test 11 IoT manufacturers and the results reveal that half of those IoT manufacturers do not have an adequate privacy policy specifically for their IoT devices. In addition, we prove that the action of two IoT devices does not comply with what they stated in their privacy policy agreement

Location histogram privacy by sensitive location hiding and target histogram avoidance/resemblance

A location histogram is comprised of the number of times a user has visited locations as they move in an area of interest, and it is often obtained from the user in the context of applications such as recommendation and advertising. However, a location histogram that leaves a user's computer or device may threaten privacy when it contains visits to locations that the user does not want to disclose (sensitive locations), or when it can be used to profile the user in a way that leads to price discrimination and unsolicited advertising (e.g. as 'wealthy' or 'minority member'). Our work introduces two privacy notions to protect a location histogram from these threats: sensitive location hiding, which aims at concealing all visits to sensitive locations, and target avoidance/resemblance, which aims at concealing the similarity/dissimilarity of the user's histogram to a target histogram that corresponds to an undesired/desired profile. We formulate an optimization problem around each notion: Sensitive Location Hiding (SLH), which seeks to construct a histogram that is as similar as possible to the user's histogram but associates all visits with nonsensitive locations, and Target Avoidance/Resemblance (TA/TR), which seeks to construct a histogram that is as dissimilar/similar as possible to a given target histogram but remains useful for getting a good response from the application that analyzes the histogram. We develop an optimal algorithm for each notion, which operates on a notion-specific search space graph and finds a shortest or longest path in the graph that corresponds to a solution histogram. In addition, we develop a greedy heuristic for the TA/TR problem, which operates directly on a user's histogram. Our experiments demonstrate that all algorithms are effective at preserving the distribution of locations in a histogram and the quality of location recommendation. They also demonstrate that the heuristic produces near-optimal solutions while being orders of magnitude faster than the optimal algorithm for TA/TR

Detecting IoT user behavior and sensitive information in encrypted IoT -app traffic

Many people use smart-home devices, also known as the Internet of Things (IoT), in their daily lives. Most IoT devices come with a companion mobile application that users need to install in their smartphone or tablet in order to control, configure, and interface with the IoT device. IoT devices send information about their users from their app directly to the IoT manufacturer's cloud; we call this the ''app-to-cloud way''. In this research, we invent a tool called IoT-app privacy inspector that can automatically infer the following from the IoT network traffic: the packet that reveals user interaction type with the IoT device via its app (e.g. login), the packets that carry sensitive Personal Identifiable Information (PII), the content type of such sensitive information (e.g. user's location). We use Random Forest classifier as a supervised machine learning algorithm to extract features from network traffic. To train and test the three different multi-class classifiers, we collect and label network traffic from different IoT devices via their apps. We obtain the following classification accuracy values for the three aforementioned types of information: 99.4%, 99.8%, and 99.8%. This tool can help IoT users take an active role in protecting their privacy