Yksityisyyden turvaavia protokollia verkkoliikenteen suojaamiseen

Digital technologies have become an essential part of our lives. In many parts of the world, activities such as socializing, providing health care, leisure and education are entirely or partially relying on the internet. Moreover, the COVID-19 world pandemic has also contributed significantly to our dependency on the on-line world. While the advancement of the internet brings many advantages, there are also disadvantages such as potential loss of privacy and security. While the users enjoy surfing on the web, service providers may collect a variety of information about their users, such as the users’ location, gender, and religion. Moreover, the attackers may try to violate the users’ security, for example, by infecting the users’ devices with malware. In this PhD dissertation, to provide means to protect networking we propose several privacy-preserving protocols. Our protocols empower internet users to get a variety of services, while at the same time ensuring users’ privacy and security in the digital world. In other words, we design our protocols such that the users only share the amount of information with the service providers that is absolutely necessary to gain the service. Moreover, our protocols only add minimal additional time and communication costs, while leveraging cryptographic schemes to ensure users’ privacy and security. The dissertation contains two main themes of protocols: privacy-preserving set operations and privacy-preserving graph queries. These protocols can be applied to a variety of application areas. We delve deeper into three application areas: privacy-preserving technologies for malware protection, protection of remote access, and protecting minors.Digitaaliteknologiasta on tullut oleellinen osa ihmisten elämää. Monissa osissa maailmaa sellaiset toiminnot kuten terveydenhuolto, vapaa-ajan vietto ja opetus ovat osittain tai kokonaan riippuvaisia internetistä. Lisäksi COVID-19 -pandemia on lisännyt ihmisten riippuvuutta tietoverkoista. Vaikkakin internetin kehittyminen on tuonut paljon hyvää, se on tuonut mukanaan myös haasteita yksityisyydelle ja tietoturvalle. Käyttäjien selatessa verkkoa palveluntarjoajat voivat kerätä käyttäjästä monenlaista tietoa, kuten esimerkiksi käyttäjän sijainnin, sukupuolen ja uskonnon. Lisäksi hyökkääjät voivat yrittää murtaa käyttäjän tietoturvan esimerkiksi asentamalla hänen koneelleen haittaohjelmia. Tässä väitöskirjassa esitellään useita turvallisuutta suojaavia protokollia tietoverkossa tapahtuvan toiminnan turvaamiseen. Nämä protokollat mahdollistavat internetin käytön monilla tavoilla samalla kun ne turvaavat käyttäjän yksityisyyden ja tietoturvan digitaalisessa maailmassa. Toisin sanoen nämä protokollat on suunniteltu siten, että käyttäjät jakavat palveluntarjoajille vain sen tiedon, joka on ehdottoman välttämätöntä palvelun tuottamiseksi. Protokollat käyttävät kryptografisia menetelmiä käyttäjän yksityisyyden sekä tietoturvan varmistamiseksi, ja ne hidastavat kommunikaatiota mahdollisimman vähän. Tämän väitöskirjan sisältämät protokollat voidaan jakaa kahteen eri teemaan: protokollat yksityisyyden suojaaville joukko-operaatioille ja protokollat yksityisyyden suojaaville graafihauille. Näitä protokollia voidaan soveltaa useilla aloilla. Näistä aloista väitöskirjassa käsitellään tarkemmin haittaohjelmilta suojautumista, etäyhteyksien suojaamista ja alaikäisten suojelemista

Approach for GDPR Compliant Detection of COVID-19 Infection Chains

While prospect of tracking mobile devices' users is widely discussed all over European countries to counteract COVID-19 propagation, we propose a Bloom filter based construction providing users' location privacy and preventing mass surveillance. We apply a solution based on Bloom filters data structure that allows a third party, a government agency, to perform some privacy-preserving set relations on a mobile telco's access logfile. By computing set relations, the government agency, given the knowledge of two identified persons, has an instrument that provides a (possible) infection chain from the initial to the final infected user no matter at which location on a worldwide scale they are. The benefit of our approach is that intermediate possible infected users can be identified and subsequently contacted by the agency. With such approach, we state that solely identities of possible infected users will be revealed and location privacy of others will be preserved. To this extent, it meets General Data Protection Regulation (GDPR)requirements in this area

How Far Removed Are You? Scalable Privacy-Preserving Estimation of Social Path Length with Social PaL

Social relationships are a natural basis on which humans make trust decisions. Online Social Networks (OSNs) are increasingly often used to let users base trust decisions on the existence and the strength of social relationships. While most OSNs allow users to discover the length of the social path to other users, they do so in a centralized way, thus requiring them to rely on the service provider and reveal their interest in each other. This paper presents Social PaL, a system supporting the privacy-preserving discovery of arbitrary-length social paths between any two social network users. We overcome the bootstrapping problem encountered in all related prior work, demonstrating that Social PaL allows its users to find all paths of length two and to discover a significant fraction of longer paths, even when only a small fraction of OSN users is in the Social PaL system - e.g., discovering 70% of all paths with only 40% of the users. We implement Social PaL using a scalable server-side architecture and a modular Android client library, allowing developers to seamlessly integrate it into their apps.Comment: A preliminary version of this paper appears in ACM WiSec 2015. This is the full versio

EsPRESSo: Efficient Privacy-Preserving Evaluation of Sample Set Similarity

Electronic information is increasingly often shared among entities without complete mutual trust. To address related security and privacy issues, a few cryptographic techniques have emerged that support privacy-preserving information sharing and retrieval. One interesting open problem in this context involves two parties that need to assess the similarity of their datasets, but are reluctant to disclose their actual content. This paper presents an efficient and provably-secure construction supporting the privacy-preserving evaluation of sample set similarity, where similarity is measured as the Jaccard index. We present two protocols: the first securely computes the (Jaccard) similarity of two sets, and the second approximates it, using MinHash techniques, with lower complexities. We show that our novel protocols are attractive in many compelling applications, including document/multimedia similarity, biometric authentication, and genetic tests. In the process, we demonstrate that our constructions are appreciably more efficient than prior work.Comment: A preliminary version of this paper was published in the Proceedings of the 7th ESORICS International Workshop on Digital Privacy Management (DPM 2012). This is the full version, appearing in the Journal of Computer Securit

P2KMV: A Privacy-preserving Counting Sketch for Efficient and Accurate Set Intersection Cardinality Estimations

In this paper, we propose P2KMV, a novel privacy-preserving counting sketch, based on the k minimum values algorithm. With P2KMV, we offer a versatile privacy-enhanced technology for obtaining statistics, following the principle of data minimization, and aiming for the sweet spot between privacy, accuracy, and computational efficiency. As our main contribution, we develop methods to perform set operations, which facilitate cardinality estimates under strong privacy requirements. Most notably, we propose an efficient, privacy-preserving algorithm to estimate the set intersection cardinality. P2KMV provides plausible deniability for all data items contained in the sketch. We discuss the algorithm's privacy guarantees as well as the accuracy of the obtained estimates. An experimental evaluation confirms our analytical expectations and provides insights regarding parameter choices