Internet users are vulnerable to privacy attacks despite the use of encryption. Webpage fingerprinting, an attack that analyzes encrypted traffic, can identify the webpages visited by a user. The key challenges in performing mass-scale webpage fingerprinting arise from (i) the sheer number of combinations of user behavior and preferences to account for, and; (ii) the bound on the number of website queries imposed by the defense mechanisms (e.g., DDoS defense) deployed at the website. These constraints preclude the use of conventional data-intensive ML-based techniques. In this work, we propose Snoopy, a first-of-its-kind framework, that performs webpage fingerprinting for a large number of users visiting a website. Snoopy caters to the generalization requirements of mass-surveillance while complying with a bound on the number of website accesses (finite query model) for traffic sample collection. We show that Snoopy achieves$\approx 90\%$accuracy when evaluated on most websites, across various browsing contexts. A simple ensemble of Snoopy and an ML-based technique achieves$\approx 97\%$accuracy while adhering to the finite query model, in cases when Snoopy alone does not perform well.

HTTP/2 introduced multi-threaded server operation for performance improvement over HTTP/1.1. Recent works have discovered that multi-threaded operation results in multiplexed object transmission, that can also have an unanticipated positive effect on TLS/SSL privacy. In fact, these works go on to design privacy schemes that rely heavily on multiplexing to obfuscate the sizes of the objects based on which the attackers inferred sensitive information. Orthogonal to these works, we examine if the privacy offered by such schemes work in practice. In this work, we show that it is possible for a network adversary with modest capabilities to completely break the privacy offered by the schemes that leverage HTTP/2 multiplexing. Our adversary works based on the following intuition: restricting only one HTTP/2 object to be in the server queue at any point of time will eliminate multiplexing of that object and any privacy benefit thereof. In our scheme, we begin by studying if (1) packet delays, (2) network jitter, (3) bandwidth limitation, and (4) targeted packet drops have an impact on the number of HTTP/2 objects processed by the server at an instant of time. Based on these insights, we design our adversary that forces the server to serialize object transmissions, thereby completing the attack. Our adversary was able to break the privacy of a real-world HTTP/2 website 90% of the time, the code for which will be released. To the best of our knowledge, this is the first privacy attack on HTTP/2.

The IT infrastructure of large organizations consists of devices and software services purchased from multiple vendors. The problem of measuring the quality of service (QoS) of each of these vendor devices (and services) is challenging since the vendors may tamper with the measurements for monetary benefits or saving debugging efforts. Existing solutions for QoS measurement in trusted environments cannot be extended for this problem since the vendors can easily circumvent them. Solutions borrowed from other areas such as client-server QoS measurement do not help either since they incur unreasonable storage and network overheads, or require extensive modifications to the packet headers. In this paper, we propose the Measuring Tape scheme, comprised of (1) a novel data structure called evidence Bloom filter (e-BF) that can be deployed at the vendor devices (and services), and (2) unique querying techniques, which can be used by the administrator to query the e-BF to measure QoS. While e-BF uses storage and computational resources judiciously, the querying techniques ensure resilience to adversarial behavior. We evaluate our solution based on a few real-world and synthetic traces and with different adversaries. Our results highlight the trade-off between resources (i.e., storage and computation) and the accuracy of QoS predictions, as well as its implications on security. We also present an analytical model of e-BF that establishes the relationship between storage, prediction accuracy, and security. Further, we present security arguments to illustrate how our solution thwarts adversarial attempts to tamper QoS.