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The rapid growth of news dissemination and user engagement on social media has raised concerns about the influence and societal impact of biased and unreliable information. As a response to these concerns, a substantial body of research has been dedicated to understanding how users interact with different news. However, this research has primarily analyzed publicly shared posts. With a significant portion of engagement taking place within Facebook's private sphere, it is therefore important to also consider the private posts. In this paper, we present the first comprehensive comparison of the interaction patterns and depth of engagement between public and private posts of different types of news content shared on Facebook. To compare these patterns, we gathered and analyzed two complementary datasets: the first includes interaction data for all Facebook posts (private + public) referencing a manually labeled collection of over 19K news articles, while the second contains only interaction data for public posts tracked by CrowdTangle. As part of our methodology, we introduce several carefully designed data processing steps that address some critical aspects missed by prior works but that (through our iterative discussions and feedback with the CrowdTangle team) emerged as important to ensure fairness for this type of study. Our findings highlight significant disparities in interaction patterns across various news classes and spheres. For example, our statistical analysis demonstrates that users engage significantly more deeply with news in the private sphere compared to the public one, underscoring the pivotal role of considering both the public and private spheres of Facebook in future research. Beyond its scholarly impact, the findings of this study can benefit Facebook content moderators, regulators, and policymakers, contributing to a healthier online discourse.

Privacy of machine learning models is one of the remaining challenges that hinder the broad adoption of Artificial Intelligent (AI). This paper considers this problem in the context of image datasets containing faces. Anonymization of such datasets is becoming increasingly important due to their central role in the training of autonomous cars, for example, and the vast amount of data generated by surveillance systems. While most prior work de-identifies facial images by modifying identity features in pixel space, we instead project the image onto the latent space of a Generative Adversarial Network (GAN) model, find the features that provide the biggest identity disentanglement, and then manipulate these features in latent space, pixel space, or both. The main contribution of the paper is the design of a feature-preserving anonymization framework, StyleID, which protects the individuals' identity, while preserving as many characteristics of the original faces in the image dataset as possible. As part of the contribution, we present a novel disentanglement metric, three complementing disentanglement methods, and new insights into identity disentanglement. StyleID provides tunable privacy, has low computational complexity, and is shown to outperform current state-of-the-art solutions., Comment: Accepted to Privacy Enhancing Technologies Symposium (PETS), July 2023. Will appear in Proceedings on Privacy Enhancing Technologies (PoPETs), volume 1, 2023. 15 pages including references and appendix, 16 figures, 5 tables

The modern Internet is highly dependent on trust communicated via certificates. However, in some cases, certificates become untrusted, and it is necessary to revoke them. In practice, the problem of secure revocation is still open. Furthermore, the existing procedures do not leave a transparent and immutable revocation history. We propose and evaluate a new revocation transparency protocol that introduces postcertificates and utilizes the existing Certificate Transparency (CT) logs. The protocol is practical, has a low deployment cost, provides an immutable history of revocations, enables delegation, and helps to detect revocation-related misbehavior by certificate authorities (CAs). With this protocol, a holder of a postcertificate can bypass the issuing CA and autonomously initiate the revocation process via submission of the postcertificate to a CT log. The CAs are required to monitor CT logs and proceed with the revocation upon detection of a postcertificate. Revocation status delivery is performed independently and with an arbitrary status protocol. Postcertificates can increase the accountability of the CAs and empower the certificate owners by giving them additional control over the status of the certificates. We evaluate the protocol, measure log and monitor performance, and conclude that it is possible to provide revocation transparency using existing CT logs.

Dual Connectivity (DC) is an important lower-layer feature accelerating the transition from 4G to 5G that also is expected to play an important role in standalone 5G radio networks. However, even though the packet reordering introduced by DC can significantly impact the performance of upper-layer protocols, no prior work has studied the impact of DC on QUIC. In this paper, we present the first such performance study. Using a series of throughput and fairness experiments, we show how QUIC is affected by different DC parameters, network conditions, and whether the DC implementation aims to improve throughput or reliability. Results for two QUIC implementations (aioquic, ngtcp2) and two congestion control algorithms (NewReno, CUBIC) are presented under both static and highly time-varying network conditions. Our findings provide network operators with insights and understanding into the impacts of splitting QUIC traffic in a DC environment. With reasonably selected DC parameters and increased UDP receive buffers, QUIC over DC performs similarly to TCP over DC and achieves optimal fairness under symmetric link conditions when DC is not used for packet duplication. The insights can help network operators provide modern users with better end-to-end service when deploying DC., Comment: 14 pages. This paper is published in Computer Networks (by Elsevier)

The modern Internet is highly dependent on the trust communicated via X.509 certificates. However, in some cases certificates become untrusted and it is necessary to revoke them. In practice, the problem of secure certificate revocation has not yet been solved, and today no revocation procedure (similar to Certificate Transparency w.r.t. certificate issuance) has been adopted to provide transparent and immutable history of all revocations. Instead, the status of most certificates can only be checked with Online Certificate Status Protocol (OCSP) and/or Certificate Revocation Lists (CRLs). In this paper, we present the first longitudinal characterization of the revocation statuses delivered by CRLs and OCSP servers from the time of certificate expiration to status disappearance. The analysis captures the status history of over 1 million revoked certificates, including 773K certificates mass-revoked by Let's Encrypt. Our characterization provides a new perspective on the Internet's revocation rates, quantifies how short-lived the revocation statuses are, highlights differences in revocation practices within and between different CAs, and captures biases and oddities in the handling of revoked certificates. Combined, the findings motivate the development and adoption of a revocation transparency standard., Comment: In proceedings of Passive and Active Measurement Conference (PAM 2021)

Internet security and privacy stand on the trustworthiness of public certificates signed by Certificate Authorities (CAs). However, software products do not trust the same CAs and therefore maintain different root stores, each typically containing hundreds of trusted roots capable of issuing "trusted" certificates for any domain. Incidents with misissued certificates motivated Google to implement and enforce Certificate Transparency (CT). CT logs archive certificates in a public, auditable and append-only manner. The adoption of CT changed the trust landscape. As a part of this change, CT logs started to maintain their own root lists and log certificates that chain back to one of the trusted roots. In this paper, we present the first characterization of this emerging CT root store landscape, as well as the tool that we developed for data collection, visualization, and analysis of the root stores. We compare the logs' root stores and quantify their changes with respect to both each other and the root stores of major software vendors, look at evolving vendor CT policies, and show that root store mismanagement may be linked to log misbehavior. Finally, we present and discuss the results of a survey that we have sent to the log operators participating in Apple's and Google's CT log programs., Comment: 9 pages

The demand and usage of 360$^{\circ}$ video services are expected to increase. However, despite these services being highly bandwidth intensive, not much is known about the potential value that basic bandwidth saving techniques such as server or edge-network on-demand caching (e.g., in a CDN) could have when used for delivery of such services. This problem is both important and complicated as client-side solutions have been developed that split the full 360$^{\circ}$ view into multiple tiles, and adapt the quality of the downloaded tiles based on the user's expected viewing direction and bandwidth conditions. This paper presents new trace-based analysis methods that incorporate users' viewports (the area of the full 360$^{\circ}$ view the user actually sees), a first characterization of the cross-user similarities of the users' viewports, and a trace-based analysis of the potential bandwidth savings that caching-based techniques may offer under different conditions. Our analysis takes into account differences in the time granularity over which viewport overlaps can be beneficial for resource saving techniques, compares and contrasts differences between video categories, and accounts for uncertainties in the network conditions and the prediction of the future viewing direction when prefetching. The results provide substantial insight into the conditions under which overlap can be considerable and caching effective, and inform the design of new caching system policies tailored for 360$^{\circ}$ video., Comment: 23 pages. This paper has been accepted (Dec. 2021) to ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM)

With 360$^{\circ}$ video, only a limited fraction of the full view is displayed at each point in time. This has prompted the design of streaming delivery techniques that allow alternative playback qualities to be delivered for each candidate viewing direction. However, while prefetching based on the user's expected viewing direction is best done close to playback deadlines, large buffers are needed to protect against shortfalls in future available bandwidth. This results in conflicting goals and an important prefetch aggressiveness tradeoff problem regarding how far ahead in time from the current playpoint prefetching should be done. This paper presents the first characterization of this tradeoff. The main contributions include an empirical characterization of head movement behavior based on data from viewing sessions of four different categories of 360$^{\circ}$ video, an optimization-based comparison of the prefetch aggressiveness tradeoffs seen for these video categories, and a data-driven discussion of further optimizations, which include a novel system design that allows both tradeoff objectives to be targeted simultaneously. By qualitatively and quantitatively analyzing the above tradeoffs, we provide insights into how to best design tomorrow's delivery systems for 360$^{\circ}$ videos, allowing content providers to reduce bandwidth costs and improve users' playback experiences., Comment: This paper is an extended version of our original ACM MMSys 2018 paper. Please cite our original paper (with the same title) published in ACM Multimedia Systems (MMSys), Amsterdam, Netherlands, June 2018, pp. 258-269

Cloud services and other shared third-party infrastructures allow individual content providers to easily scale their services based on current resource demands. In this paper, we consider an individual content provider that wants to minimize its delivery costs under the assumptions that the storage and bandwidth resources it requires are elastic, the content provider only pays for the resources that it consumes, and costs are proportional to the resource usage. Within this context, we (i) derive worst-case bounds for the optimal cost and competitive cost ratios of different classes of "cache on $M^{th}$ request" cache insertion policies, (ii) derive explicit average cost expressions and bounds under arbitrary inter-request distributions, (iii) derive explicit average cost expressions and bounds for short-tailed (deterministic, Erlang, and exponential) and heavy-tailed (Pareto) inter-request distributions, and (iv) present numeric and trace-based evaluations that reveal insights into the relative cost performance of the policies. Our results show that a window-based "cache on $2^{nd}$ request" policy using a single threshold optimized to minimize worst-case costs provides good average performance across the different distributions and the full parameter ranges of each considered distribution, making it an attractive choice for a wide range of practical conditions where request rates of individual file objects typically are not known and can change quickly., Comment: To appear in IFIP Performance, Dec. 2018, Toulouse, France. The final version will appear in Performance Evaluation, volumes 127-128, Nov. 2018, pp. 70-92

Content-delivery applications can achieve scalability and reduce wide-area network traffic using geographically distributed caches. However, each deployed cache has an associated cost, and under time-varying request rates (e.g., a daily cycle) there may be long periods when the request rate from the local region is not high enough to justify this cost. Cloud computing offers a solution to problems of this kind, by supporting dynamic allocation and release of resources. In this paper, we analyze the potential benefits from dynamically instantiating caches using resources from cloud service providers. We develop novel analytic caching models that accommodate time-varying request rates, transient behavior as a cache fills following instantiation, and selective cache insertion policies. Within the context of a simple cost model, we then develop bounds and compare policies with optimized parameter selections to obtain insights into key cost/performance tradeoffs. We find that dynamic cache instantiation can provide substantial cost reductions, that potential reductions strongly dependent on the object popularity skew, and that selective cache insertion can be even more beneficial in this context than with conventional edge caches. Finally, our contributions also include accurate and easy-to-compute approximations that are shown applicable to LRU caches under time-varying workloads., Comment: 17 pages. Authors' version. To appear in IEEE Transactions on Cloud Computing, accepted Sept. 2021

Video dissemination through sites such as YouTube can have widespread impacts on opinions, thoughts, and cultures. Not all videos will reach the same popularity and have the same impact. Popularity differences arise not only because of differences in video content, but also because of other "content-agnostic" factors. The latter factors are of considerable interest but it has been difficult to accurately study them. For example, videos uploaded by users with large social networks may tend to be more popular because they tend to have more interesting content, not because social network size has a substantial direct impact on popularity. In this paper, we develop and apply a methodology that is able to accurately assess, both qualitatively and quantitatively, the impacts of various content-agnostic factors on video popularity. When controlling for video content, we observe a strong linear "rich-get-richer" behavior, with the total number of previous views as the most important factor except for very young videos. The second most important factor is found to be video age. We analyze a number of phenomena that may contribute to rich-get-richer, including the first-mover advantage, and search bias towards popular videos. For young videos we find that factors other than the total number of previous views, such as uploader characteristics and number of keywords, become relatively more important. Our findings also confirm that inaccurate conclusions can be reached when not controlling for content., Comment: Dataset available at: http://www.ida.liu.se/~nikca/papers/kdd12.html