Hiding mobile traffic fingerprints with GLOVE

Proceeding of: 11th ACM Conference on Emerging Networking Experiments and Technologies ACM (CoNEXT 2015), Heidelberg, Germany, 1-4 December 2015Preservation of user privacy is paramount in the publication of datasets that contain fine-grained information about individuals. The problem is especially critical in the case of mobile traffic datasets collected by cellular operators, as they feature high subscriber trajectory uniqueness and they are resistant to anonymization through spatiotemporal generalization. In this work, we first unveil the reasons behind such undesirable features of mobile traffic datasets, by leveraging an original measure of the anonymizability of users' mobile fingerprints. Building on such findings, we propose GLOVE, an algorithm that grants k-anonymity of trajectories through specialized generalization. We evaluate our methodology on two nationwide mobile traffic datasets, and show that it achieves k-anonymity while preserving a substantial level of accuracy in the data.This work was supported by the French National Research Agency under grant ANR-13-INFR-0005 ABCD and by the EU FP7 ERA-NET program under grant CHIST-ERA-2012 MACACO

GLOVE: towards privacy-preserving publishing of record-level-truthful mobile phone trajectories

Datasets of mobile phone trajectories collected by network operators offer an unprecedented opportunity to discover new knowledge from the activity of large populations of millions. However, publishing such trajectories also raises significant privacy concerns, as they contain personal data in the form of individual movement patterns. Privacy risks induce network operators to enforce restrictive confidential agreements in the rare occasions when they grant access to collected trajectories, whereas a less involved circulation of these data would fuel research and enable reproducibility in many disciplines. In this work, we contribute a building block toward the design of privacy-preserving datasets of mobile phone trajectories that are truthful at the record level. We present GLOVE, an algorithm that implements k-anonymity, hence solving the crucial unicity problem that affects this type of data while ensuring that the anonymized trajectories correspond to real-life users. GLOVE builds on original insights about the root causes behind the undesirable unicity of mobile phone trajectories, and leverages generalization and suppression to remove them. Proof-of-concept validations with large-scale real-world datasets demonstrate that the approach adopted by GLOVE allows preserving a substantial level of accuracy in the data, higher than that granted by previous methodologies.This work was supported by the Atracción de Talento Investigador program of the Comunidad de Madrid under Grant No. 2019-T1/TIC-16037 NetSense

A survey on privacy in human mobility

In the last years we have witnessed a pervasive use of location-aware technologies such as vehicular GPS-enabled devices, RFID based tools, mobile phones, etc which generate collection and storing of a large amount of human mobility data. The powerful of this data has been recognized by both the scientific community and the industrial worlds. Human mobility data can be used for different scopes such as urban traffic management, urban planning, urban pollution estimation, etc. Unfortunately, data describing human mobility is sensitive, because people's whereabouts may allow re-identification of individuals in a de-identified database and the access to the places visited by indi-viduals may enable the inference of sensitive information such as religious belief, sexual preferences, health conditions, and so on. The literature reports many approaches aimed at overcoming privacy issues in mobility data, thus in this survey we discuss the advancements on privacy-preserving mo-bility data publishing. We first describe the adversarial attack and privacy models typically taken into consideration for mobility data, then we present frameworks for the privacy risk assessment and finally, we discuss three main categories of privacy-preserving strategies: methods based on anonymization of mobility data, methods based on the differential privacy models and methods which protect privacy by exploiting generative models for synthetic trajectory generation

Towards private and robust machine learning for information security

Many problems in information security are pattern recognition problems. For example, determining if a digital communication can be trusted amounts to certifying that the communication does not carry malicious or secret content, which can be distilled into the problem of recognising the difference between benign and malicious content. At a high level, machine learning is the study of how patterns are formed within data, and how learning these patterns generalises beyond the potentially limited data pool at a practitioner’s disposal, and so has become a powerful tool in information security. In this work, we study the benefits machine learning can bring to two problems in information security. Firstly, we show that machine learning can be used to detect which websites are visited by an internet user over an encrypted connection. By analysing timing and packet size information of encrypted network traffic, we train a machine learning model that predicts the target website given a stream of encrypted network traffic, even if browsing is performed over an anonymous communication network. Secondly, in addition to studying how machine learning can be used to design attacks, we study how it can be used to solve the problem of hiding information within a cover medium, such as an image or an audio recording, which is commonly referred to as steganography. How well an algorithm can hide information within a cover medium amounts to how well the algorithm models and exploits areas of redundancy. This can again be reduced to a pattern recognition problem, and so we apply machine learning to design a steganographic algorithm that efficiently hides a secret message with an image. Following this, we proceed with discussions surrounding why machine learning is not a panacea for information security, and can be an attack vector in and of itself. We show that machine learning can leak private and sensitive information about the data it used to learn, and how malicious actors can exploit vulnerabilities in these learning algorithms to compel them to exhibit adversarial behaviours. Finally, we examine the problem of the disconnect between image recognition systems learned by humans and by machine learning models. While human classification of an image is relatively robust to noise, machine learning models do not possess this property. We show how an attacker can cause targeted misclassifications against an entire data distribution by exploiting this property, and go onto introduce a mitigation that ameliorates this undesirable trait of machine learning

Privacy in trajectory micro-data publishing : a survey

We survey the literature on the privacy of trajectory micro-data, i.e., spatiotemporal information about the mobility of individuals, whose collection is becoming increasingly simple and frequent thanks to emerging information and communication technologies. The focus of our review is on privacy-preserving data publishing (PPDP), i.e., the publication of databases of trajectory micro-data that preserve the privacy of the monitored individuals. We classify and present the literature of attacks against trajectory micro-data, as well as solutions proposed to date for protecting databases from such attacks. This paper serves as an introductory reading on a critical subject in an era of growing awareness about privacy risks connected to digital services, and provides insights into open problems and future directions for research.Comment: Accepted for publication at Transactions for Data Privac