Foundations, Properties, and Security Applications of Puzzles: A Survey

Cryptographic algorithms have been used not only to create robust ciphertexts but also to generate cryptograms that, contrary to the classic goal of cryptography, are meant to be broken. These cryptograms, generally called puzzles, require the use of a certain amount of resources to be solved, hence introducing a cost that is often regarded as a time delay---though it could involve other metrics as well, such as bandwidth. These powerful features have made puzzles the core of many security protocols, acquiring increasing importance in the IT security landscape. The concept of a puzzle has subsequently been extended to other types of schemes that do not use cryptographic functions, such as CAPTCHAs, which are used to discriminate humans from machines. Overall, puzzles have experienced a renewed interest with the advent of Bitcoin, which uses a CPU-intensive puzzle as proof of work. In this paper, we provide a comprehensive study of the most important puzzle construction schemes available in the literature, categorizing them according to several attributes, such as resource type, verification type, and applications. We have redefined the term puzzle by collecting and integrating the scattered notions used in different works, to cover all the existing applications. Moreover, we provide an overview of the possible applications, identifying key requirements and different design approaches. Finally, we highlight the features and limitations of each approach, providing a useful guide for the future development of new puzzle schemes.Comment: This article has been accepted for publication in ACM Computing Survey

Information-Theoretic Secure Outsourced Computation in Distributed Systems

Secure multi-party computation (secure MPC) has been established as the de facto paradigm for protecting privacy in distributed computation. One of the earliest secure MPC primitives is the Shamir\u27s secret sharing (SSS) scheme. SSS has many advantages over other popular secure MPC primitives like garbled circuits (GC) -- it provides information-theoretic security guarantee, requires no complex long-integer operations, and often leads to more efficient protocols. Nonetheless, SSS receives less attention in the signal processing community because SSS requires a larger number of honest participants, making it prone to collusion attacks. In this dissertation, I propose an agent-based computing framework using SSS to protect privacy in distributed signal processing. There are three main contributions to this dissertation. First, the proposed computing framework is shown to be significantly more efficient than GC. Second, a novel game-theoretical framework is proposed to analyze different types of collusion attacks. Third, using the proposed game-theoretical framework, specific mechanism designs are developed to deter collusion attacks in a fully distributed manner. Specifically, for a collusion attack with known detectors, I analyze it as games between secret owners and show that the attack can be effectively deterred by an explicit retaliation mechanism. For a general attack without detectors, I expand the scope of the game to include the computing agents and provide deterrence through deceptive collusion requests. The correctness and privacy of the protocols are proved under a covert adversarial model. Our experimental results demonstrate the efficiency of SSS-based protocols and the validity of our mechanism design

Game Theory in Distributed Systems Security: Foundations, Challenges, and Future Directions

Many of our critical infrastructure systems and personal computing systems have a distributed computing systems structure. The incentives to attack them have been growing rapidly as has their attack surface due to increasing levels of connectedness. Therefore, we feel it is time to bring in rigorous reasoning to secure such systems. The distributed system security and the game theory technical communities can come together to effectively address this challenge. In this article, we lay out the foundations from each that we can build upon to achieve our goals. Next, we describe a set of research challenges for the community, organized into three categories -- analytical, systems, and integration challenges, each with "short term" time horizon (2-3 years) and "long term" (5-10 years) items. This article was conceived of through a community discussion at the 2022 NSF SaTC PI meeting.Comment: 11 pages in IEEE Computer Society magazine format, including references and author bios. There is 1 figur

Online advertising: analysis of privacy threats and protection approaches

Online advertising, the pillar of the “free” content on the Web, has revolutionized the marketing business in recent years by creating a myriad of new opportunities for advertisers to reach potential customers. The current advertising model builds upon an intricate infrastructure composed of a variety of intermediary entities and technologies whose main aim is to deliver personalized ads. For this purpose, a wealth of user data is collected, aggregated, processed and traded behind the scenes at an unprecedented rate. Despite the enormous value of online advertising, however, the intrusiveness and ubiquity of these practices prompt serious privacy concerns. This article surveys the online advertising infrastructure and its supporting technologies, and presents a thorough overview of the underlying privacy risks and the solutions that may mitigate them. We first analyze the threats and potential privacy attackers in this scenario of online advertising. In particular, we examine the main components of the advertising infrastructure in terms of tracking capabilities, data collection, aggregation level and privacy risk, and overview the tracking and data-sharing technologies employed by these components. Then, we conduct a comprehensive survey of the most relevant privacy mechanisms, and classify and compare them on the basis of their privacy guarantees and impact on the Web.Peer ReviewedPostprint (author's final draft

Addressing Automated Adversaries of Network Applications

The Internet supports a perpetually evolving patchwork of network services and applications. Popular applications include the World Wide Web, online commerce, online banking, email, instant messaging, multimedia streaming, and online video games. Practically all networked applications have a common objective: to directly or indirectly process requests generated by humans. Some users employ automation to establish an unfair advantage over non-automated users. The perceived and substantive damages that automated, adversarial users inflict on an application degrade its enjoyment and usability by legitimate users, and result in reputation and revenue loss for the application\u27s service provider. This dissertation examines three challenges critical to addressing the undesirable automation of networked applications. The first challenge explores individual methods that detect various automated behaviors. Detection methods range from observing unusual network-level request traffic to sensing anomalous client operation at the application-level. Since many detection methods are not individually conclusive, the second challenge investigates how to combine detection methods to accurately identify automated adversaries. The third challenge considers how to leverage the available knowledge to disincentivize adversary automation by nullifying their advantage over legitimate users. The thesis of this dissertation is that: there exist methods to detect automated behaviors with which an application\u27s service provider can identify and then systematically disincentivize automated adversaries. This dissertation evaluates this thesis using research performed on two network applications that have different access to the client software: Web-based services and multiplayer online games

Partisan narratives on the 2016 US presidential election: a critical geopolitical analysis of Russian interference

As the Cold War drew to a close, the German sociologist Ulrich Beck coined the concept of reflexive modernization to describe the structural risks inadvertently produced by modernity’s progress. Through the approach of critical geopolitics, such risks radically began to transform traditional understanding of space and territory, allegedly deterritorializing traditional spatial structures, such as nation-states. However, scholars maintain that the process of reterritorialization, defined as the “inscription of new boundaries” reattaching space to “newly imagined visions of state, territory, and community,” cyclically follow deterritorialization (Albert 1991, 61; Ó Tuathail 1996, 230). Nevertheless, few scholars in the field of International Relations (IR), have seriously analyzed the process of reterritorialization. However, following the 2016 US presidential election, popular discourse in the US on Russian interference appeared to reterritorialize previously deterritorialized space, such as cyber and information space, by likening Russian hacks, leaks and collusion to the violation of the sovereign territory of the US. Thus, this thesis aims to research how US popular discourse reterritorializes Russian interference in the 2016 US presidential election, while comparing and contrasting the partisan narratives constructed in light of the political polarization of the US in recent years. To achieve this goal, a discourse analysis is conducted on storylines from 30 online news articles, from three right-wing and three left-wing media outlets. As hypothesized, the analysis confirms that both partisan narratives reterritorialize previously deterritorialized risks associated with reflexive modernization, transcribe the storylines into traditional US geopolitical culture, and call for assertive measures towards Russia which violated US territory, as well as towards internal Others, which weakened US territory.http://www.ester.ee/record=b5147583*es