In this paper, we consider user location privacy in mobile edge clouds (MECs). MECs are small clouds deployed at the network edge to offer cloud services close to mobile users, and many solutions have been proposed to maximize service locality by migrating services to follow their users. Co-location of a user and his service, however, implies that a cyber eavesdropper observing service migrations between MECs can localize the user up to one MEC coverage area, which can be fairly small (e.g., a femtocell). We consider using chaff services to defend against such an eavesdropper, with focus on strategies to control the chaffs. Assuming the eavesdropper performs maximum likelihood (ML) detection, we consider both heuristic strategies that mimic the user's mobility and optimized strategies designed to minimize the detection or tracking accuracy. We show that a single chaff controlled by the optimal strategy or its online variation can drive the eavesdropper's tracking accuracy to zero when the user's mobility is sufficiently random. We further propose extended strategies that utilize randomization to defend against an advanced eavesdropper aware of the strategy. The efficacy of our solutions is verified through both synthetic and trace-driven simulations.
@cite_14: 
@cite_10: The maximum throughput of relaying information flows while concealing their presence is studied. The concealment is achieved by embedding transmissions of information flows into truly independent transmission schedules that resemble the normal transmission behaviors without any flow. Such embedding may reduce the throughput for delay-sensitive flows, and the paper provides a quantitative characterization of the level of reduction. Under a strict or average delay constraint, the maximum normalized throughput is measured by the efficiency of the optimal relay algorithms that embed the most flow into given transmission schedules. Exact analytical solutions and closed-form approximations are derived for renewal schedules, verified by simulations on both synthetic traffic and traces. The results reveal general relationships between the clandestine throughput and system parameters including delay constraints, traffic load, and traffic distributions. In particular, the throughput is found to be negatively related to the burstiness of the cover traffic. Moreover, simulations show that the throughputs of renewal traffic with certain power-law interarrival distributions can closely approximate those of actual traces.
@cite_25: Cloud-based applications benefit from the scalability and efficiency offered by server consolidation and shared facilities. However, the shared nature of cloud infrastructures may introduce threats stemming from the co-location and combination of untrusted components, in addition to typical risks due to the inevitable presence of weaknesses in the infrastructure itself. As a result, adversaries may be able to place themselves in monitoring proximity to high-value targets and gain unauthorized access to sensitive data. In this paper we present DIGIT, a system that employs decoy computation to impede the ability of adversaries to take advantage of unauthorized access to sensitive information. DIGIT introduces uncertainly as to which data and computation is legitimate by generating a mix of real and decoy activity within a cloud application. Although DIGIT may not impede intruders indefinitely, it prevents them from determining whether a captured system is handling actual or bogus processing within a reasonable amount of time. As adversaries cannot easily distinguish between real and decoy activity, they have to either risk triggering beacon-bearing data that can be traced back to them, or expend significant effort to pinpoint any actual data of interest, forcing them to reveal their presence.
@cite_4: Cloud computing promises to significantly change the way we use computers and access and store our personal and business information. With these new computing and communications paradigms arise new data security challenges. Existing data protection mechanisms such as encryption have failed in preventing data theft attacks, especially those perpetrated by an insider to the cloud provider. We propose a different approach for securing data in the cloud using offensive decoy technology. We monitor data access in the cloud and detect abnormal data access patterns. When unauthorized access is suspected and then verified using challenge questions, we launch a disinformation attack by returning large amounts of decoy information to the attacker. This protects against the misuse of the user's real data. Experiments conducted in a local file setting provide evidence that this approach may provide unprecedented levels of user data security in a Cloud environment.
The idea of using chaffs to protect user security privacy has been explored in other contexts. In communication networks, @cite_14 uses dummy packets as chaffs to hide the true traffic rates, and @cite_10 furthers the idea to hide the transmission patterns of multi-hop flows. In cloud computing, @cite_4 proposes to use decoy data to protect the real data during a data theft attack. Similarly, @cite_25 proposes to use decoy applications running on fake inputs to confuse an insider attacker. However, we are the first to study the use of chaff services to protect user location privacy. Besides the novel application context, our problem also requires new methodology. Specifically, as a real service needs to migrate dynamically to follow a mobile user, its mobility pattern (in addition to its content) can be used to identify the service. To effectively protect the user, the chaff services have to resemble the real service in both content and mobility. =-1