Coupons and discounts are essential in modern marketing to attract new customers and improve sales. Digital marketing has further amplified their impact, making electronic coupons (e-coupons) a key strategy for targeted promotions. However, e-coupons are vulnerable to fraud. Counterfeit coupons, phishing scams, and malware attacks pose risks to both businesses and customers. These threats emphasize the need for secure and privacy-preserving e-coupon systems. Proximity marketing, which uses location-based technologies to engage customers in real-time, has emerged as an effective tool for targeted promotions. Businesses can send personalized offers to customers in proximity, enhancing engagement and conversion rates. This paper presents a privacy-preserving e-coupon application that integrates location-based marketing while maintaining user anonymity. Our approach enables businesses to measure the impact of e-coupon distribution on marketing by providing fine-grained location-based statistics without compromising user privacy. The security and privacy of the protocol for the privacy-preserving e-coupon application are formally verified using ProVerif.

Fake base stations (FBSs) can carry out attacks against mobile devices and user equipment (UE). We tackle the problem of localizing an FBS after its existence has been detected. Karaçay et al. (2021) have presented a localization method based on the values of the Reference Signal Received Power (RSRP), reported in the standardized measurement reports of the 3rd Generation Partnership Project (3GPP) and sent by the UEs. We show that the method they have proposed fails if the FBS uses a directional antenna (instead of an omnidirectional antenna). The localization method by Karaçay et al. is based on half-planes. We propose a new method that localizes FBSs using a directional antenna by first employing a half-plane method and then applying a method based on convex hulls. Our tests for the new method on data derived from Network Simulator 3 (ns-3) show that it significantly outperforms the previous method for a directional FBS antenna.

We introduce PQ 5G AKA, a prototype for possible extensions of the current 5G authentication and key agreement protocol to the post-quantum setting, we further analyse the computational and communication complexities of our prototype using potential post-quantum KEMs

Quantum computing is a serious threat for contemporary cryptography and, in order to address this threat, key sizes used for symmetric cryptography should be doubled, most typically from 128 to 256 bits. The third-generation partnership project (3GPP), the standards organization for mobile communications, is currently in the process of adding support for 256-bit keys into future mobile standards for the purpose of confidentiality and integrity protection. The current standard defines three ciphers with 128-bit keys: AES, SNOW 3G, and ZUC. There are two competing stream ciphers for the replacement of the 128-bit SNOW 3G: a 256-bit variant of SNOW 3G and a new design SNOW-V. SNOW-V has superior performance in software platforms. In this article, we investigate the implementations of these ciphers on field-programmable gate arrays (FPGAs) for both confidentiality and integrity protection. We develop optimized architectures and introduce a new parallelization technique that applies to both ciphers and offers significant throughput improvements in particular when the ciphers are used in the integrity protection modes. Our results show that SNOW-V has a significant performance advantage over the 256-bit SNOW 3G also on FPGAs.

The rapid expansion of available online services has raised concerns about user privacy. In the online world, only a minority of users is actually aware where their data is stored and the policies, how the data may be eventually used. However, at the same time consumers expect more quality from online services, demanding personalized services that fit their individual needs, preferences and values. One approach for service personalization is to use collaborative recommenders. From the privacy perspective, mainstream collaborative recommenders present an inherent security risk, since they are based on memorizing user-item transactions. In this paper, we will study a recently developed token-based method (sometimes referred as an acronym "upcv") which creates privacy-protecting abstraction that is based on collections of randomly generated tokens. These collections are capable of providing information for collaborative recommendations without maintaining any transactional history. This paper presents quality evaluation of item-to-item recommendations using the token-based collaborative recommender, utilizing ISBN agencies of Book-Crossing dataset (BX) books at the data set. This paper will also discuss challenges related to BX. Privacy issues are evaluated with a specific emphasis on the concept of deniability.

Modern computing has increasingly been moved into network-based solutions such as clouds. The security in these solutions is not always at satisfactory level. Ideas to improve the security of networked computing include the technique called garbled circuits or garbling, a technique first introduced by Yao and then formalized by Bellare, Hoang and Rogaway. The security of garbling has been considered from different perspectives, including adaptivity and reusability. In this paper, we improve the practicality of garbling by presenting a new type of adaptivity with support to reusability. We also show how this new type of adaptivity is related to the known security concepts.

We investigate the problem of estimating the transmission time of fragmented messages over multiple disrupted links. We build a system model for the case where a single message is sent over a chain of links and the disruptions in these links are identically and independently distributed. For this case, we derive an approximation formula for the mean transmission time, based on number of links, length of fragments and distributions of disruptions. The formula is verified against simulation experiments in the cases of uniform and exponential distributions for disruptions.

Progress in mobile wireless technology has resulted in the increased use of mobile devices to store and manage users' personal schedules. Users also access popular context-based services, typically provided by third-party providers, by using these devices for social networking, dating and activity-partner searching applications. Very often, these applications need to determine common availabilities among a set of user schedules. The privacy of the scheduling operation is paramount to the success of such applications, as often users do not want to share their personal schedules with other users or third-parties. Previous research has resulted in solutions that provide privacy guarantees, but they are either too complex or do not fit well in the popular user-provider operational model. In this paper, we propose practical and privacy-preserving solutions to the server-based scheduling problem. Our novel algorithms take advantage of the homomorphic properties of well-known cryptosystems in order to privately compute common user availabilities. We also formally outline the privacy requirements in such scheduling applications and we implement our solutions on real mobile devices. The experimental measurements and analytical results show that the proposed solutions not only satisfy the privacy properties but also fare better, in regard to computation and communication efficiency, compared to other well-known solutions.

A Mobile Ad Hoc Network (MANET) is becoming a practical platform for pervasive social networking. People chat with each other via MANET for instant social activities. How to help mobile users to build up trust in such an environment is becoming an important issue. We develop a reputation system for pervasive social networking. We implement it by applying Nokia N900 smartphones as MANET nodes based on a distributed energy-efficient social networking platform. Our system can indicate each user's local reputation during chatting. It also provides detailed reputation information and supports on-chat voting and afterwards voting. This demo paper introduces the system design and its principal functionalities.

Mobile services are increasingly utilizing context information, e.g. user location. There is an intriguing interplay between privacy, identity, security and trust in this domain. Technologies are presented that can help in managing this interplay. Several of these technologies have also been tested in trials carried out recently by Nokia Research Center in Lausanne, Switzerland. We take a look at some key results obtained from these trials. Examples of used technologies are pseudonym management for location privacy purposes, usage control for protection of privacy-sensitive context data and secure multiparty computations for minimizing the amount of needed information exchange.

Pervasive social networks extend traditional social networking by enabling users to share information in a peer-to-peer fashion using their wireless mobile devices. Contrary to traditional online social networks, privacy protection in such networks depends heavily on users' context (time, location, activity, etc.) and their sensitivity to the shared data and context. Existing privacy-preserving mechanisms do not adapt well to different data, context and user sensitivities. In this work, we follow a fresh approach for privacy preservation, called privacy-triggered communications; it allows users in such pervasive networks to dynamically regulate their communications based on their context and on the evolution of their privacy in that context. Our initial results show that this is a feasible strategy for privacy management in pervasive social networking scenarios.

We show how a standard deck of playing cards can be used to implement a secure multiparty protocol to compute any boolean function. Our contribution to previous work: no identical copies of cards are needed, and the number of necessary cards is reduced.