While in-network caching is an essential feature of Information Centric Networks (ICN) for improved content dissemination and reducing the bandwidth consumption at the core of the network, it is prone to many privacy treats. For example, an adversary can passively breach the privacy of a consumer by simply analyzing the different retrieval times for the same content. We aim to address this problem of timing analysis attacks by developing a differentially private off-path caching strategy.

Consumer mobility is implicitly supported in ICN while producer mobility is an important challenge. In this paper, we design a mechanism to support producer mobility using the spatial locality of moving producers and reverse paths of data. Then, we model the consumer (requesting) application as a GI/M/c/N queue which is used to estimate system parameters like content delivery time distribution.

With the ever-growing mobile data traffic, providing user-mobility has become a necessity. While consumer mobility is implicitly handled in NDN, producer mobility is still of one the main challenges. In this paper we propose a hop-count based forwarding strategy to support seamless producer mobility. The key idea of this strategy is- the router makes a decision based on the number of hops traveled by the interest whether to forward the interest using Forwarding Information Base (FIB) entries or broadcast it. The intuition behind this strategy being two-fold- spatial locality of producer and data packets follow the reverse path of interests in NDN.

Almost all modern online services run on geo-distributed data centers, and fault tolerance is one of the primary requirement that decides the revenue of the service provider. Recent experiences have shown that the failure of a data center (at a site) is inevitable. In order to mask the failure, spare compute capacity needs to be provisioned across the distributed data center, which leads to additional cost. While the existing literature addresses the capacity provisioning problem only to minimize the number of servers, we argue that the operating cost needs to be considered as well. Since the operating cost and client demand vary both across space and time, we consider cost-aware capacity provisioning to account for their impact on the operating cost of data centers.