Instant Messaging (IM) applications such as Signal, Telegram, and WhatsApp have become tremendously popular in recent years. Unfortunately, such IM services have been targets of governmental surveillance and censorship, as these services are home to public and private communications on socially and politically sensitive topics. To protect their clients, popular IM services deploy state-of-the-art encryption. Despite the use of advanced encryption, we show that popular IM applications leak sensitive information about their clients to adversaries merely monitoring their encrypted IM traffic, with no need for leveraging any software vulnerabilities of IM applications. Specifically, we devise traffic analysis attacks enabling an adversary to identify participants of target IM communications (e.g., forums) with high accuracies. We believe that our study demonstrates a significant, real-world threat to the users of such services. We demonstrate the practicality of our attacks through extensive experiments on real-world IM communications. We show that standard countermeasure techniques can degrade the effectiveness of these attacks. We hope our study will encourage IM providers to integrate effective traffic obfuscation into their software. In the meantime, we have designed a countermeasure system, called IMProxy that can be used by IM clients with no need for any support from IM providers. We demonstrate the effectiveness of IMProxy through simulation and experiments.

We employ a resource block (RB1)-based compression method using neural networks for the channel coefficients under the specification of the third generation partnership project (3GPP). An autoencoder is trained to compress/decompress the channel coefficients, and the same compressor/decompressor is used for all RBs. Because the proposed method is RBbased, it universally applies to various combinations of resource configurations allowed by 3GPP. It is essential to compress the channel coefficients because they are stored in a buffer that takes a large memory when a large bandwidth is allocated. The buffer provides the channel coefficients to different blocks of the baseband modem. We investigate the compression considering two formats for the complex channel coefficients, Cartesian and polar. For each case, we train and test a separate autoencoder to maximize the compression performance. We reduce the buffer size by about 3 times without losing the performance by more than 0.1 dB, and the proposed algorithm is reliably applicable to both new radio (NR) and long-term evolution (LTE).1A set of 12 consecutive subcarriers

Covert communication conceals the existence of the transmission from a watchful adversary. We consider the fundamental limits for covert communications via packet insertion over packet channels whose packet timings are governed by a renewal process of rate λ. Authorized transmitter Jack sends packets to authorized receiver Steve, and covert transmitter Alice wishes to transmit packets to covert receiver Bob without being detected by watchful adversaries Willie 1 and Willie 2. Willies cannot authenticate the source of the packets or collaborate. Hence, each Willie looks for statistical anomalies in the packet stream from Jack to Steve to attempt detection of unauthorized packet insertion. First, we consider a special case where the packet timings are governed by a Poisson process and we show that Alice can covertly insert O(√λT) packets for Bob in a time interval of length T; conversely, if Alice inserts ω(√λT) packets, she will be detected by Willie 1 or Willie 2 with high probability. Then, we extend our results to general renewal channels and show that in a stream of N packets transmitted by Jack, Alice can covertly insert O(√N) packets; if she inserts ω(√N) packets, she will be detected by Willie 1 or Willie 2, with high probability.

Network flow fingerprinting can be used to de-anonymize communications on anonymity systems such as Tor by linking the ingress and egress segments of anonymized connections. Assume Alice and Bob have access to the input and the output links of an anonymous network, respectively, and they wish to collaboratively reveal the connections between the input and the output links without being detected by Willie who protects the network. Alice generates a codebook where each codeword is a unique fingerprint indicating a sequence of interpacket delays, and shares it only with Bob. To trace each flow, Alice selects a fingerprint and manipulates the packet timings of the flow to follow the packet timings suggested by the fingerprint, and Bob extracts the fingerprints from it after it passes through the network. We model the network as parallel M/M/1 queues where each queue is shared by a flow fifrom Alice to Bob and other flows independent of fi. Packet timings of the flows are governed by independent Poisson processes. Assuming all input flows have equal packet rates and that Bob observes only flows with fingerprints, we first present two scenarios: 1) Alice fingerprints all the flows and 2) Alice fingerprints a subset of the flows, unknown to Willie. Then, we extend the construction and analysis to the case of arbitrary flow rates and the case where Bob observes flows with and without fingerprints. For each scenario, we derive the number of flows that Alice and Bob can trace by fingerprinting.

The rapid growth of the Internet of Things (IoT) necessitates employing privacy-preserving techniques to protect users' sensitive information. Even when user traces are anonymized, statistical matching can be employed to infer sensitive information. In our previous work, we have established the privacy requirements for the case that the user traces are instantiations of discrete random variables and the adversary knows only the structure of the dependency graph, i.e., whether each pair of users is connected. In this paper, we consider the case where data traces are instantiations of Gaussian random variables and the adversary knows not only the structure of the graph but also the pairwise correlation coefficients. We establish the requirements on anonymization to thwart such statistical matching, which demonstrate the significant degree to which knowledge of the pairwise correlation coefficients further significantly aids the adversary in breaking user anonymity.

Covert communication conceals the transmission of the message from an attentive adversary. Recent work on the limits of covert communication in additive white Gaussian noise channels has demonstrated that a covert transmitter (Alice) can reliably transmit a maximum of O(√n) bits to a covert receiver (Bob) without being detected by an adversary (Warden Willie) in n channel uses. This paper focuses on the scenario where other “friendly” nodes distributed according to a two-dimensional Poisson point process with density m are present. We propose a strategy where the friendly node closest to the adversary, without close coordination with Alice, produces artificial noise. We show that this method allows Alice to reliably and covertly send O(min{n, mγ/2√n}) bits to Bob in n channel uses, where γ is the path-loss exponent. We also consider a setting where there are Nw collaborating adversaries uniformly and randomly located in the environment and show that in n channel uses, Alice can reliably and covertly send O(min{n, (mγ/2√n/Nwγ)}) bits to Bob when γ>2, and O(min{n, (m√n/Nw2log2Nw)}) when γ=2. Conversely, we demonstrate that no higher covert throughput is possible for γ>2.