While rich medical, behavioral, and socio-demographic data are key to modern data-driven research, their collection and use raise legitimate privacy concerns. Anonymizing datasets through de-identification and sampling before sharing them has been the main tool used to address those concerns. We here propose a generative copula-based method that can accurately estimate the likelihood of a specific person to be correctly re-identified, even in a heavily incomplete dataset. On 210 populations, our method obtains AUC scores for predicting individual uniqueness ranging from 0.84 to 0.97, with low false-discovery rate. Using our model, we find that 99.98% of Americans would be correctly re-identified in any dataset using 15 demographic attributes. Our results suggest that even heavily sampled anonymized datasets are unlikely to satisfy the modern standards for anonymization set forth by GDPR and seriously challenge the technical and legal adequacy of the de-identification release-and-forget model.

/pdf/estimating-the-success-of-re-identifications-in-incomplete-2aud3fz9xb.pdf

Estimating the success of re-identifications in incomplete datasets using generative models

Event logs recorded during the execution of business processes constitute a valuable source of information. Applying process mining techniques to them, event logs may reveal the actual process execution and enable reasoning on quantitative or qualitative process properties. However, event logs often contain sensitive information that could be related to individual process stakeholders through background information and cross-correlation. We therefore argue that, when publishing event logs, the risk of such re-identification attacks must be considered. In this paper, we show how to quantify the re-identification risk with measures for the individual uniqueness in event logs. We also report on a large-scale study that explored the individual uniqueness in a collection of publicly available event logs. Our results suggest that potentially up to all of the cases in an event log may be re-identified, which highlights the importance of privacy-preserving techniques in process mining.

/pdf/quantifying-the-re-identification-risk-of-event-logs-for-4e3q2j5u2y.pdf

Quantifying the Re-identification Risk of Event Logs for Process Mining

Web Privacy Measurement (WPM) has been established as an academic research field since 2012. WPM scholars observe websites and services to detect, characterize, and quantify privacy-impacting behaviors. The main goal of the research field is to increase transparency through measurement.Robbert J. van Eijk investigates the advertisements online that seem to follow you. The technology enabling the advertisements is called Real-Time Bidding (RTB). An RTB system is defined as a network of partners enabling big data applications within the organizational field of marketing. The system aims to improve sales by real-time data-driven marketing and personalized (behavioral) advertising. The author applies network science algorithms to arrive at measuring the privacy component of RTB. In the thesis, it is shown that cluster-edge betweenness and node betweenness support us in understanding the partnerships of the ad-technology companies. From his research it transpires that the interconnection between partners in an RTB network is caused by the data flows of the companies themselves due to their specializations in ad technology. Furthermore, the author provides that a Graph-Based Methodological Approach (GBMA) controls the situation of differences in consent implementations in European countries. The GBMA is tested on a dataset of national and regional European news websites.

https://scholarlypublications.universiteitleiden.nl/access/item%3A2970644/view

Web privacy measurement in real-time bidding systems. A graph-based approach to RTB system classification

Privacy on the Internet has become a major concern in recent years and is an ethical dilemma for Internet based applications. Social Sensing based Edge Computing (SSEC)is a category of distributed applications in which privately owned devices at the “edge” of the network participate in data collection and processing. Traditionally in SSEC, it has been difficult to manage privacy without making a tradeoff with performance or accuracy. In this paper, we present Privacy-aware Edge Computing (PEC), a framework that utilizes ring signatures for implementing social sensing applications with full privacy and without sacrificing data quality. Under PEC, privacy is managed by the edge devices without the need of a third party trusted authority. We evaluate the performance of PEC on a realworld edge computing platform and through extensive simulation studies. The results demonstrate that PEC achieves a high level of anonymity with a constrained cryptographic overhead.

Privacy-Aware Edge Computing in Social Sensing Applications Using Ring Signatures

Many websites rely on third parties for services (e.g., DNS, CDN, etc.). However, it also exposes them to shared risks from attacks (e.g., Mirai DDoS attack [24]) or cascading failures (e.g., GlobalSign revocation error [21]). Motivated by such incidents, we analyze the prevalence and impact of third-party dependencies, focusing on three critical infrastructure services: DNS, CDN, and certificate revocation checking by CA. We analyze both direct (e.g., Twitter uses Dyn) and indirect (e.g., Netflix uses Symantec as CA which uses Verisign for DNS) dependencies. We also take two snapshots in 2016 and 2020 to understand how the dependencies evolved. Our key findings are: (1) 89% of the Alexa top-100K websites critically depend on third-party DNS, CDN, or CA providers i.e., if these providers go down, these websites could suffer service disruption; (2) the use of third-party services is concentrated, and the top-3 providers of CDN, DNS, or CA services can affect 50%-70% of the top-100K websites; (3) indirect dependencies amplify the impact of popular CDN and DNS providers by up to 25X; and (4) some third-party dependencies and concentration increased marginally between 2016 to 2020. Based on our findings, we derive key implications for different stakeholders in the web ecosystem.

https://dl.acm.org/doi/pdf/10.1145/3419394.3423664

Analyzing Third Party Service Dependencies in Modern Web Services: Have We Learned from the Mirai-Dyn Incident?

We present research on the security of the most popular websites, ranked according to Alexa's top one million list, based on an HTTP response headers analysis For each of the domains included in the list, we made four different requests: an HTTP/11 request to the domain itself and to its "www" subdomain and two more equivalent HTTPS requests Redirections were always followed A detailed discussion of the request process and main outcomes is presented, including X509 certificate issues and comparison of results with equivalent HTTP/2 requests The body of the responses was discarded, and the HTTP response header fields were stored in a database We analysed the prevalence of the most important response headers related to web security aspects In particular, we took into account Strict- Transport-Security, Content-Security-Policy, X-XSS-Protection, X-Frame-Options, Set-Cookie (for session cookies) and X-Content-Type We also reviewed the contents of response HTTP headers that potentially could reveal unwanted information, like Server (and related headers), Date and Referrer-Policy This research offers an up-to-date survey of current prevalence of web security policies implemented through HTTP response headers and concludes that most popular sites tend to implement it noticeably more often than less popular ones Equally, HTTPS sites seem to be far more eager to implement those policies than HTTP only websites A comparison with previous works show that web security policies based on HTTP response headers are continuously growing, but still far from satisfactory widespread adoption

HTTP security headers analysis of top one million websites

Over the recent years public transportation systems around the world have been migrating to digital ticketing solutions. This paper investigates security and privacy aspects of the one such system implemented by Riga municipality called e-talons by analysing published open data containing ride registrations.

Privacy violations in Riga open data public transport system

Devices that are connected to the Internet are of great interest to malicious parties and security researchers alike, as direct remote reachability places them at the highest risk of being attacked, compromised or otherwise abused. To investigate the root causes of the security risks it is necessary to understand what classes of devices are reachable and how they are affected. These devices are numerous and heterogeneous, making it impractical to classify large sets by applying static rules. We have explored which features of the HTTP response headers are useful and what kinds of indicators can be extracted by analysing a whole Internet scan for the HTTP ports 80 and 8080. We propose improvements for manually labelling training sets for use in training neural networks. The results of this research have been applied in creating a labelling tool enabling us to combine different features and markers together, thus decreasing manual workload.

Investigating HTTP response headers for the classification of devices on the Internet

Devices that are connected to the Internet are very interesting to security researchers as are at high risk of being attacked, compromised or otherwise abused. To investigate the root causes of the risks it is necessary to understand what classes of devices are affected in different ways. These devices are heterogeneous, thus making it impractical to classify large sets by applying static rules. We propose improvements for manually labelling training sets using HTTP response features for future classification using a neural network.

Exploring features of HTTP responses for the classification of devices on the Internet

Hundreds of millions of devices are directly reachable by anyone on the Internet Security researchers and malicious actors are highly interested in ICS, IoT, and building automation and networking devices that can be compromised to negatively affect either a specific person or organization or a whole country at once The current approach for determining a class of individual device is to conduct a manual investigation or apply static rules to large sets of devices, which is timeconsuming and ineffective We are proposing to utilize neural networks for automated classificationMany devices have a generic web interface supporting HTTP protocol We have investigated which features of the HTTP responses from these devices are meaningful for training the neural network model and enabling classification of devices We have trained neural network models and assessed their accuracy to be 87% We are analysing the classified sets of the whole Internet scans consisting of tens of millions of devices and comparing them between the years 2018 and 2019 to identify the changes This kind of all-encompassing view might reveal positive and negative trends that are happening to specific classes of devices, which might be correlated with real-world events, eg new policies issued by governments

Arturs Lavrenovs

Papers

HTTP security headers analysis of top one million websites

Privacy violations in Riga open data public transport system

Investigating HTTP response headers for the classification of devices on the Internet

Exploring features of HTTP responses for the classification of devices on the Internet

Towards Classifying Devices on the Internet Using Artificial Intelligence