Abstract

Abstract

Data is the new oil for the car industry. Cars generate data about how they are used and who’s behind the wheel which gives rise to a novel way of profiling individuals. Several prior works have successfully demonstrated the feasibility of driver re-identification using the in-vehicle network data captured on the vehicle’s CAN bus. However, all of them used signals (e.g., velocity, brake pedal or accelerator position) that have already been extracted from the CAN log which is itself not a straightforward process. Indeed, car manufacturers intentionally do not reveal the exact signal location within CAN logs. Nevertheless, we show that signals can be efficiently extracted from CAN logs using machine learning techniques. We exploit that signals have several distinguishing statistical features which can be learnt and effectively used to identify them across different vehicles, that is, to quasi ”reverse-engineer” the CAN protocol. We also demonstrate that the extracted signals can be successfully used to re-identify individuals in a dataset of 33 drivers. Therefore, hiding signal locations in CAN logs per se does not prevent them to be regarded as personal data of drivers.

Abstract

Machine learning algorithms have reached mainstream status and are widely deployed in many applications. The accuracy of such algorithms depends significantly on the size of the underlying training dataset; in reality a small or medium sized organization often does not have the necessary data to train a reasonably accurate model. For such organizations, a realistic solution is to train their machine learning models based on their joint dataset (which is a union of the individual ones). Unfortunately, privacy concerns prevent them from straightforwardly doing so. While a number of privacy-preserving solutions exist for collaborating organizations to securely aggregate the parameters in the process of training the models, we are not aware of any work that provides a rational framework for the participants to precisely balance the privacy loss and accuracy gain in their collaboration. In this paper, by focusing on a two-player setting, we model the collaborative training process as a two-player game where each player aims to achieve higher accuracy while preserving the privacy of its own dataset. We introduce the notion of Price of Privacy, a novel approach for measuring the impact of privacy protection on the accuracy in the proposed framework. Furthermore, we develop a game-theoretical model for different player types, and then either find or prove the existence of a Nash Equilibrium with regard to the strength of privacy protection for each player. Using recommendation systems as our main use case, we demonstrate how two players can make practical use of the proposed theoretical framework, including setting up the parameters and approximating the non-trivial Nash Equilibrium.

Abstract

Abstract

Abstract

Third-party applications on Facebook can collect personal data of the users who install them, but also of their friends. This raises serious privacy issues as these friends are not notified by the applications nor by Facebook and they have not given consent. This paper presents a detailed multi-faceted study on the collateral information collection of the applications on Facebook. To investigate the views of the users, we designed a questionnaire and collected the responses of 114 participants. The results show that participants are concerned about the collateral information collection and in particular about the lack of notification and of mechanisms to control the data collection. Based on real data, we compute the likelihood of collateral information collection affecting users: we show that the probability is significant and greater than 80% for popular applications such as TripAdvisor. We also demonstrate that a substantial amount of profile data can be collected by applications, which enables application providers to profile users. To investigate whether collateral information collection is an issue to users’ privacy we analysed the legal framework in light of the General Data Protection Regulation. We provide a detailed analysis of the entities involved and investigate which entity is accountable for the collateral information collection. To provide countermeasures, we propose a privacy dashboard extension that implements privacy scoring computations to enhance transparency toward collateral information collection. Furthermore, we discuss alternative solutions highlighting other countermeasures such as notification and access control mechanisms, cryptographic solutions and application auditing. To the best of our knowledge this is the first work that provides a detailed multi-faceted study of this problem and that analyses the threat of user profiling by application providers.

Abstract

Machine learning algorithms have reached mainstream status and are widely deployed in many applications. The accuracy of such algorithms depends significantly on the size of the underlying training dataset; in reality a small or medium sized organization often does not have enough data to train a reasonably accurate model. For such organizations, a realistic solution is to train machine learning models based on a joint dataset (which is a union of the individual ones). Unfortunately, privacy concerns prevent them from straightforwardly doing so. While a number of privacy-preserving solutions exist for collaborating organizations to securely aggregate the parameters in the process of training the models, we are not aware of any work that provides a rational framework for the participants to precisely balance the privacy loss and accuracy gain in their collaboration. In this paper, we model the collaborative training process as a two-player game where each player aims to achieve higher accuracy while preserving the privacy of its own dataset. We introduce the notion of Price of Privacy, a novel approach for measuring the impact of privacy protection on the accuracy in the proposed framework. Furthermore, we develop a game-theoretical model for different player types, and then either find or prove the existence of a Nash Equilibrium with regard to the strength of privacy protection for each player.

Abstract

In today’s digital society, increasing amounts of contextually rich spatio-temporal information are collected and used, e.g., for knowledge-based decision making, research purposes, optimizing operational phases of city management, planning infrastructure networks, or developing timetables for public transportation with an increasingly autonomous vehicle fleet. At the same time, however, publishing or sharing spatio-temporal data, even in aggregated form, is not always viable owing to the danger of violating individuals’ privacy, along with the related legal and ethical repercussions. In this chapter, we review some fundamental approaches for anonymizing and releasing spatio-temporal density, i.e., the number of individuals visiting a given set of locations as a function of time. These approaches follow different privacy models providing different privacy guarantees as well as accuracy of the released anonymized data. We demonstrate some sanitization (anonymization) techniques with provable privacy guarantees by releasing the spatio-temporal density of Paris, in France. We conclude that, in order to achieve meaningful accuracy, the sanitization process has to be carefully customized to the application and public characteristics of the spatio-temporal data.

Abstract

Bla

Abstract

Third-party apps enable a personalized experience on social networking platforms; however, they give rise to privacy interdependence issues. Apps installed by a user’s friends can collect and potentially misuse her personal data inflicting collateral damage on the user while leaving her without proper means of control. In this paper, we present a multi-faceted study on the collateral information collection of apps in social networks. We conduct a user survey and show that Facebook users are concerned about this issue and the lack of mechanisms to control it. Based on real data, we compute the likelihood of collateral information collection affecting users; we show that the probability is significant and depends on both the friendship network and the popularity of the app. We also show its significance by computing the proportion of exposed user attributes including the case of profiling, when several apps are offered by the same provider. Finally, we propose a privacy dashboard concept enabling users to control the collateral damage.

Abstract

Flexible service delivery is a key requirement for 5G network architectures. This includes the support for collaborative service delivery by multiple operators, when an individual operator lacks the geographical footprint or the available network, compute or storage resources to provide the requested service to its customer. Network Function Virtualisation is a key enabler of such service delivery, as network functions (VNFs) can be outsourced to other operators. Owing to the (partial lack of) contractual relationships and co-opetition in the ecosystem, the privacy of user data, operator policy and even VNF code could be compromised. In this paper, we present a case for privacy in a VNF-enabled collaborative service delivery architecture. Specifically, we show the promise of homomorphic encryption (HE) in this context and its performance limitations through a proof of concept implementation of an image transcoder network function. Furthermore, inspired by application-specific encryption techniques, we propose a way forward for private, payload-intensive VNFs.

Abstract

Abstract

Abstract

Users share massive amounts of personal information and opinion with each other and different service providers every day. In such an interconnected setting, the privacy of individual users is bound to be affected by the decisions of others, giving rise to the phenomenon which we term as interdependent privacy. In this paper we define online privacy interdependence, show its existence through a study of Facebook application permissions, and model its impact through an Interdependent Privacy Game (IPG). We show that the arising negative externalities can steer the system into equilibria which are inefficient for both users and platform vendor. We also discuss how the underlying incentive misalignment, the absence of risk signals and low user awareness contribute to unfavorable outcomes.