Differential Defense Against Distributed Timing Attack for Privacy-Preserving Information Centric Network

Information-Centric Networking (ICN) is an emerging networking paradigm where named and routable data (content) is the focal point. Users send explicit requests (interests) which specify content by name, and the network handles routing these interests to some entity capable of satisfying them with the appropriate data response (producer). One key feature of ICN is opportunistic in-network content caching. This property facilitates efficient content distribution by reducing bandwidth consumption, lessening network congestion, and improving the content retrieval latency by users (consumers). Unfortunately, the same feature is also detrimental to privacy of content consumers and producers. Simple to implement, and difficult to detect, timing attacks can exploit ICN routers as “oracles” and allow an adversary to learn whether a nearby consumer recently requested certain content. The attack leverages a timing side channel that relies on router caches and is implemented by requesting a few packets from each piece of content being probed. Similarly, probing attacks that target content producers can be used to discover whether certain content has been recently distributed. After analyzing the scope and feasibility of such attacks, we propose and evaluate some efficient countermeasures that offer quantifiable privacy guarantees while retaining the benefits of ICN.

With the rapid growth of internet traffic, the traditional host-to-host TCP/IP architecture is subject to many service limitations faced with content-oriented applications. Various novel network architectures have been proposed to solve these limitations, among which Information-Centric Networking (ICN) is one of the most prominent. ICN features the decoupling of content (service) from the physical devices storing (providing) it through location-independent naming, and offers inherent enhancement to network performance, such as multicast and in-network caching. ICN in-network caching has been extensively studied, and we believe that it may also be the main incentive for ISPs to deploy ICN. A CDN (content delivery network) is a typical content-oriented network paradigm that aims to provide the fast delivery of content. In this paper, we leverage the advantages of the in-network caching of ICN to enhance the content delivery efficiency of CDN by integrating ICN as a service. First, we present our design of a content delivery network enhanced with ICN, called IECDN. Additionally, we formulate a mathematical model to optimize the performance of our proposed design and conduct a series of evaluations. The results indicate that our proposed design provides significant performance gains while reducing bandwidth consumption and shows better resilience to traffic surge.

Personal data privacy is a great concern by governments across the world as citizens generate huge amount of data continuously and industries using this for betterment of user centric services. There must be a reasonable balance between data privacy and utility of data. Differential privacy is a promise by data collector to the customer’s personal privacy. Centralised Differential Privacy (CDP) is performing output perturbation of user’s data by applying required privacy budget. This promises the inclusion or exclusion of individual’s data in data set not going to create significant change for a statistical query output and it offers -Differential privacy guarantee. CDP is holding a strong belief on trusted data collector and applying global sensitivity of the data. Local Differential Privacy (LDP) helps user to locally perturb his data and there by guaranteeing privacy even with untrusted data collector. Many differential privacy algorithms handles parameters like privacy budget, sensitivity and data utility in different ways and mostly trying to keep trade-off between privacy and utility of data. This paper evaluates differential privacy algorithms in regard to the privacy support it offers according to the sensitivity of the data. Generalized application of privacy budget is found ineffective in comparison to the sensitivity based usage of privacy budget.

Current Internet architecture is becoming inadequate for new requirements of highly scalable and efficient distribution of contents. Information Centric Networking (ICN) is one of the alternatives for the Next Generation Internet (NGI), which focuses mainly on contents. In-network caching is one of the major attributes of ICN, which allows contents to be cached in any ICN node. Any user can access ICN contents from different distributed locations. This attribute maximizes the problem of unauthorized access to ICN contents. In this paper, we propose a Decentralized Access Control Protocol for ICN architectures (DACPI). In this protocol, fewer public messages are needed for access control enforcement between ICN subscribers and ICN nodes than the existing access control protocols. DACPI depends on ICN self-certifying naming scheme. We perform security analysis on DACPI for the following attacks: man-in-the-middle, forward security, replay attacks, integrity, and privacy violations. According to the security analysis, DACPI prevents unauthorized access to ICN contents with fewer messages passed.

This article describes how the named data networking (NDN) has recently received a lot of attention as a potential information-centric networking (ICN) architecture for the future Internet. The NDN paradigm has a great potential to efficiently address and solve the current seminal IP-based IoT architecture issues and requirements. NDN can be used with different sets of caching algorithms and caching replacement policies. The authors investigate the most suitable combination of these two features to be implemented in an IoT environment. For this purpose, the authors first reviewed the current research and development progress in ICN, then they conduct a qualitative comparative study of the relevant ICN proposals and discuss the suitability of the NDN as a promising architecture for IoT. Finally, they evaluate the performance of NDN in an IoT environment with different caching algorithms and replacement policies. The obtained results show that the consumer-cache caching algorithm used with the Random Replacement (RR) policy significantly improve NDN content validity in an IoT environment.

Gradient leakage attacks are considered one of the wickedest privacy threats in deep learning as attackers covertly spy gradient updates during iterative training without compromising model training quality, and yet secretly reconstruct sensitive training data using leaked gradients with high attack success rate. Although deep learning with differential privacy is a defacto standard for publishing deep learning models with differential privacy guarantee, we show that differentially private algorithms with fixed privacy parameters are vulnerable against gradient leakage attacks. This paper investigates alternative approaches to gradient leakage resilient deep learning with differential privacy (DP). <i>First</i>, we analyze existing implementation of deep learning with differential privacy, which use fixed noise variance to injects constant noise to the gradients in all layers using fixed privacy parameters. Despite the DP guarantee provided, the method suffers from low accuracy and is vulnerable to gradient leakage attacks. <i>Second</i>, we present a gradient leakage resilient deep learning approach with differential privacy guarantee by using dynamic privacy parameters. Unlike fixed-parameter strategies that result in constant noise variance, different dynamic parameter strategies present alternative techniques to introduce adaptive noise variance and adaptive noise injection which are closely aligned to the trend of gradient updates during differentially private model training. <i>Finally</i>, we describe four complementary metrics to evaluate and compare alternative approaches. Extensive experiments on six benchmark datasets show that differentially private deep learning with dynamic privacy parameters outperforms the deep learning using fixed DP parameters, and existing adaptive clipping approaches in all aspects: compelling accuracy performance, strong differential privacy guarantee, and high attack resilience.

Information-centric networking (ICN) brings in-network caching as a significant characteristic along with its novel network architecture. In-network caching serves as an efficient content distribution strategy in which intermediate routers store and redistribute data to reduce network latency, congestion, and retrieval time. Meanwhile, it decouples contents from the publisher, and content integrity and confidentiality turn out to be problematic. In this study, we propose a mechanism named “in-network secure content management” (ISCM) to facilitate content integrity and authenticity in ICN by exploiting identity-based cryptography (IBC) for content signature verification. In addition, it guarantees content confidentiality by applying a hybrid encryption-based access control mechanism to prevent unauthorized content access and tampering attacks. Evaluation results reveal that ISCM offers secure content retrieval in ICN with a scalable key distribution without significant computational overhead and communication delay.

Information-centric networking (ICN) is developed for the future Internet because of the tremendous increase of content demands in the Internet. In the ICN architecture, in-network storage for caching plays an important role in improving content delivery efficiency, scalability and availability. To enjoy the benefits of caching users’ preferable contents without disclosing the users’ privacy, in this chapter, we aim to integrate local differential privacy (LDP) techniques into data-driven optimization, and propose a novel scheme to allow content provider (CP) to collect the locally differentially private content preferences of a selected group of users, exploit data-driven approach to predict the content popularity, and offer the cache-enabled access points (APs) economic incentives to cache the selected preferable content. Here, optimized local hashing (OLH) is employed to locally add differential private noise to the users’ preference content information and the noisy data is sent to the CP. Besides, we leverage data-driven methodology to predict the content popularity according to the constructed reference distribution of the given noisy preference content data from users. We formulate a data-driven caching revenue optimization, provide feasible solutions, and conduct simulations to show the effectiveness of the proposed scheme.

Information-centric networking (ICN) is developed for the future Internet because of the tremendous increase of content demands in the Internet. In the ICN architecture, in-network storage for caching plays an important role in improving content delivery efficiency, scalability and availability. To enjoy the benefits of caching users' preferable contents without disclosing the users' privacy, in this paper, we aim to integrate local differential privacy (LDP) techniques into data-driven optimization, and propose a novel scheme to allow content provider (CP) to collect the locally differentially private content preferences of a selected group of users, exploit data-driven approach to predict the content popularity, and offer the cache-enabled access points (APs) economic incentives to cache the selected preferable content. Here, optimized local hashing (OLH) is employed to locally add differential private noise to the users' preference content information and the noisy data is sent to the CP. Besides, we leverage data-driven methodology to predict the content popularity according to the constructed reference distribution of the given noisy preference content data from users. We formulate a data-driven caching revenue optimization, provide feasible solutions, and conduct simulations to show the effectiveness of the proposed scheme.

We design a content-centric privacy scheme for Information-Centric Networking (ICN). We enhance ICN's ability to support data confidentiality by introducing attribute-based encryption into ICN and making it specific to the data attributes. Our approach is unusual in that it preserves ICN's goal to decouple publishers and subscribers for greater data accessibility, scalable multiparty communication and efficient data distribution. Inspired by application-layer publish-subscribe, we enable fine-grained access control with more expressive policies. Moreover, we propose an attribute-based routing scheme that offers interest confidentiality. A prototype system is implemented based on CCNx, a popular open source version of ICN, to showcase privacy preservation in Smart Neighborhood and Smart City applications.

Information Centric Networking (ICN) is a new and promising network concept which is founded upon the idea that most users in the internet are interested in accessing digital objects, irrespectively of their locations. Digital objects in ICN have unique names that are used in order to route data content from the source node to the destination node. During the content delivery from sources to destinations, the data contents are cached to intermediate nodes in order to achieve efficient and reliable distribution of the data content among the network infrastructure (in-network caching). In this research project we focus on the efficiency of ICN for delivering Big Data with Persistent Identifiers. We proposed a Mapping Architecture for resolving PIDs to ICN names and we evaluate the efficiency of in-network caching when delivering Big Data objects. Our results showed that in-network caching can offer significant performance benefits when the cache size of the network elements that perform in-network caching is bigger than the Big Data object size.

As the number of mobile devices and IoT (internet of things) devices has explosively increased, various contents are created anytime and anywhere. To meet such trend, current internet architecture has exposed many limitations such as high control overhead due to multistaged address resolution, frequent location updates, and network congestion. Recently, information centric networking (ICN) is considered as new networking architecture to redesign current internet’s content exchange paradigm. In current ICN architecture, whenever a mobile node moves to a new domain, it needs long latency to configure and confirm the temporary name prefix. So, this paper presents an efficient name prefix configuration mechanism in mobile CCN to reduce the latency needed for the name prefix configuration during MCS’s handover. From the performance analysis, the proposed mechanism is shown to provide lower control overhead and lower resource consumption.

Information-Centric Networking (ICN) is a promising candidate architecture for the future internet that leverages the content name instead of the source of the content (host address). ICN decouples the content from its original location and owner, and enables in-network caching at the network layer. Similarly, the inception of ioT has enabled futuristic applications and services in almost every walk of life. Due to the nature of Internet of Things (IoT) applications, traditional TCP/IP-based internet architecture faces challenges such as addressing, heterogeneity, availability, scalability, and resource constraints. Therefore, ICN can be a suitable choice to address these challenges in IoT applications. Among other features of ICN, the in-network caching feature increases the availability and efficiency of content-sharing in IoT networks. Therefore, it is essential to have efficient cache placement strategies in ICN to increase its utility. in this regard, in this article, we propose an ICN cache placement strategy at the edge network for IoT applications. More precisely, we design a distributed cache placement scheme that aims to push the popular content to the edge network and keep the relatively less-popular content at the core. We also propose a collaborative mechanism to fetch content from the nearest neighbor's content-store as well as propose a cache replacement policy based on the content popularity. Through extensive simulations, we evaluate the proposed scheme and the obtained results show the efficiency and out-performance against similar strategies in the literature.

Information-Centric Networking (ICN) is a prominent topic in current networking research. ICN design significantly considers the increased demand of scalable and efficient content distribution for Future Internet. However, intermittently connected mobile environments or disruptive networks present a significant challenge to ICN deployment. In this context, delay tolerant networking (DTN) architecture is an initiative that effectively deals with network disruptions. Among all ICN proposals, Content Centric Networking (CCN) is gaining more and more interest for its architectural design, but still has the limitation in highly disruptive environment. In this paper, we design a protocol stack referred as CCNDTN which integrates DTN architecture in the native CCN to deal with network disruption. We also present the implementation details of the proposed CCNDTN. We extend CCN routing strategies by integrating Bundle protocol of DTN architecture. The integration of CCN and DTN enriches the connectivity options of CCN architecture in fragmented networks. Furthermore, CCNDTN can be beneficial through the simultaneous use of all available connectivities and opportunistic networking of DTN for the dissemination of larger data items. This paper also highlights the potential use cases of CCNDTN architecture and crucial questions about integrating CCN and DTN.

Wireless information-centric networks consider storage one of the network primitives, and propose to cache data within the network in order to improve latency to access content and reduce bandwidth consumption. We study the throughput capacity of an information-centric network when the data cached in each node has a limited lifetime. The results show that with some fixed request and cache expiration rates, the network can have the maximum throughput order of 1/√n and 1/log n in cases of grid and random networks, respectively. Comparing these values with the corresponding throughput with no cache capability (1/n and 1/√(n log n) respectively), we can actually quantify the asymptotic advantage of caching. Moreover, since the request rates will decrease as a result of increasing download delays, increasing the content lifetimes according to the network growth may result in higher throughput capacities.