Search

Cyberspace is a fragile construct threatened by malicious cyber operations of different actors, with vulnerabilities in IT hardware and software forming the basis for such activities, thus also posing a threat to global IT security. Advancements in the field of artificial intelligence accelerate this development, either with artificial intelligence enabled cyber weapons, automated cyber defense measures, or artificial intelligence-based threat and vulnerability detection. Especially state actors, with their long-term strategic security interests, often stockpile such knowledge of vulnerabilities and exploits to enable their military or intelligence service cyberspace operations. While treaties and regulations to limit these developments and to enhance global IT security by disclosing vulnerabilities are currently being discussed on the international level, these efforts are hindered by state concerns about the disclosure of unique knowledge and about giving up tactical advantages. This leads to a situation where multiple states are likely to stockpile at least some identical exploits, with technical measures to enable a depletion process for these stockpiles that preserve state secrecy interests and consider the special constraints of interacting states as well as the requirements within such environments being non-existent. This paper proposes such a privacy-preserving approach that allows multiple state parties to privately compare their stock of vulnerabilities and exploits to check for items that occur in multiple stockpiles without revealing them so that their disclosure can be considered. We call our system ExTRUST and show that it is scalable and can withstand several attack scenarios. Beyond the intergovernmental setting, ExTRUST can also be used for other zero-trust use cases, such as bug-bounty programs., Comment: 16 pages, 3 figures, IEEE Transactions on Technology and Society

Over the last two years, governments all over the world have used a variety of containment measures to control the spread of COVID-19, such as contact tracing, social distance regulations, and curfews. Epidemiological simulations are commonly used to assess the impact of those policies before they are implemented in actuality. Unfortunately, their predictive accuracy is hampered by the scarcity of relevant empirical data, concretely detailed social contact graphs. As this data is inherently privacy-critical, there is an urgent need for a method to perform powerful epidemiological simulations on real-world contact graphs without disclosing sensitive information. In this work, we present RIPPLE, a privacy-preserving epidemiological modeling framework that enables the execution of a wide range of standard epidemiological models for any infectious disease on a population's most recent real contact graph while keeping all contact information private locally on the participants' devices. In this regard, we also present PIR-SUM, a novel extension to private information retrieval that allows users to securely download the sum of a set of elements from a database rather than individual elements. Our theoretical constructs are supported by a proof-of-concept implementation in which we show that a 2-week simulation over a population of half a million can be finished in 7 minutes with each participant consuming less than 50 KB of data.

The present paper addresses the task of reliably identifying critical contacts by using COVID-19 tracing apps. A reliable classification is crucial to ensure a high level of protection, and at the same time to prevent many people from being sent to quarantine by the app. Tracing apps are based on the capabilities of current smartphones to enable a broadest possible availability. Existing capabilities of smartphones include the exchange of Bluetooth Low Energy (BLE) signals and of audio signals, as well as the use of gyroscopes and magnetic sensors. The Bluetooth power measurements, which are often used today, may be complemented by audio ranging and attitude estimation in the future. Smartphones are worn in different ways, often in pockets and bags, which makes the propagation of signals and thus the classification rather unpredictable. Relying on the cooperation of users to wear their phones hanging from their neck would change the situation considerably. In this case the performance, achievable with BLE and audio measurements, becomes predictable. Our analysis identifies parameters that result in accurate warnings, at least within the scope of validity of the models. A significant reduction of the spreading of the disease can then be achieved by the apps, without causing many people to unduly go to quarantine. The present paper is the first of three papers which analyze the situation in some detail., Comment: Two references added wrto. version of August 2nd: [7] and [10] in new numbering

As a response to the global COVID-19 surge in 2020, many countries have implemented lockdown or stay-at-home policies. If, however, the contact persons of every infected patient could be identified, the number of virus transmissions could be reduced, while the more incisive measures could be softened. For this purpose, contact tracing using smartphones is being considered as a promising technique. Here, smartphones emit and scan for Bluetooth Low Energy (BLE) signals for detecting devices in range. When a device is detected, its distance is estimated by evaluating its received signal strength. The main insight that is exploited for distance estimation is that the attenuation of a signal increases with the distance along which it has traveled. However, besides distance, there are multiple additional factors that impact the attenuation and hence disturb distance estimation. Among them, frequency-selective hardware and signal propagation belong to the most significant ones. For example, a BLE device transmits beacons on three different frequencies (channels), while the transmit power and the receiver sensitivity depend on the frequency. As a result, the received signal strength varies for each channel, even when the distance remains constant. However, the information on which wireless channel a beacon has been received is not made available to a smartphone. Hence, this error cannot be compensated, e.g., by calibration. In this paper, we for the first time provide a solution to detect the wireless channel on which a packet has been received on a smartphone. We experimentally evaluate our proposed technique on multiple different smartphone models. Our results help to make contact tracing more robust by improving the accuracy of distance estimation.

The control of the COVID-19 pandemic requires a considerable reduction of contacts mostly achieved by imposing movement control up to the level of enforced quarantine. This has lead to a collapse of substantial parts of the economy. Carriers of the disease are infectious roughly 3 days after exposure to the virus. First symptoms occur later or not at all. As a consequence tracing the contacts of people identified as carriers is essential for controlling the pandemic. This tracing must work everywhere, in particular indoors, where people are closest to each other. Furthermore, it should respect people's privacy. The present paper presents a method to enable a thorough traceability with very little risk on privacy. In our opinion, the latter capabilities are necessary to control the pandemic during a future relaunch of our economy., Comment: 5 pages, no figures