Data-Driven Caching with Users' Local Differential Privacy in Information-Centric Networks

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.

The project is about to implement Internet of Things (IoT) technology in Railways. Goal is to constitute a secure IoT system by using Information Centric Networking (ICN) architecture to comprehend the key requirements of IoT system. The proposed system is based on ICN architecture. ICN is the architecture which supports multicast, mobility and security. Mobility is the necessity here in the proposed IoT system in railways. System contains passenger information which should remain secure. Hence, ICN architecture is used as a base for IoT system in railways. The Internet of Things (IoT) based system is for catering passengers' services and requests in long journey railways. The system is to collect requests from passengers from each coach, track the location of crew members within train and allocate them requests from passengers. This allocation of the requests to the crew members takes place depending on the service arrival time, location information and workload factor of crew member. As proposed project is based on ICN architecture, it will use secure device discovery concept to make system more secure.

In local differential privacy (LDP), each user perturbs her data locally before sending the noisy data to a data collector. The latter then analyzes the data to obtain useful statistics. Unlike the setting of centralized differential privacy, in LDP the data collector never gains access to the exact values of sensitive data, which protects not only the privacy of data contributors but also the collector itself against the risk of potential data leakage. Existing LDP solutions in the literature are mostly limited to the case that each user possesses a tuple of numeric or categorical values, and the data collector computes basic statistics such as counts or mean values. To the best of our knowledge, no existing work tackles more complex data mining tasks such as heavy hitter discovery over set-valued data. In this paper, we present a systematic study of heavy hitter mining under LDP. We first review existing solutions, extend them to the heavy hitter estimation, and explain why their effectiveness is limited. We then propose LDPMiner, a two-phase mechanism for obtaining accurate heavy hitters with LDP. The main idea is to first gather a candidate set of heavy hitters using a portion of the privacy budget, and focus the remaining budget on refining the candidate set in a second phase, which is much more efficient budget-wise than obtaining the heavy hitters directly from the whole dataset. We provide both in-depth theoretical analysis and extensive experiments to compare LDPMiner against adaptations of previous solutions. The results show that LDPMiner significantly improves over existing methods. More importantly, LDPMiner successfully identifies the majority true heavy hitters in practical settings.

Information Centric Networks (ICN) has emerged as a new Internet paradigm where the users are being provided seamless access to contents rather than the host as in classical approach. For ICN realization, the current Internet requires architectural changes. These changes must be incorporated such that existing applications continue running without any difficulties. In this paper, we introduce a novel ICN architecture, called Overlay Information Centric Networks (O-ICN), which separates the data plane from the control plane activities of the network. Unlike the usual ICN architectures, in our approach naming and routing (control plane) functionalities have been segregated from the caching (data plane) functionality at each ICN router. We introduce a new module called an ICN Manager which is an extended version of Domain Name System (DNS) server and performs name resolution along with routing functionalities for ICN type of requests as well as the normal DNS resolution for non-ICN type of requests. The O-ICN architecture can be deployed incrementally over the current Internet and can operate both as ICN and non-ICN/legacy network simultaneously. This can also be implemented both in Software Defined Networks (SDN) as well as non-SDN networks without much modifications. The proposed architecture provides benefits in terms of simplification of network management, vendor independent solution, better use of network resources in terms of bandwidth and delay, and reduction of deployment/operational cost of the network.

Information-Centric Networking (ICN) and Software-Defined Networking (SDN) are both new evolving network architectures that are receiving a lot of attention from researchers. ICN is a Future Internet architecture which tries to transform the current Internet architecture from location- and host-centric to content-centric, where obtaining requested data is achieved by the contents’ names regardless of the location of the data. From another angle, SDN is considered a new Internet architecture that moves the control plane management from network devices to a centralized controller. The SDN controller enhances network robustness and improves its scalability, reliability, and flexibility. The integration of ICN and SDN results in massive benefits, where SDN enhances ICN networks’ manageability, controllability, and functionality, and ICN reshapes the SDN design to make it compatible with ICN features and to enhance ICN in terms of network caching, routing, mobility, and security.. In this review paper, a comprehensive survey of the issues and challenges of integrating ICN and SDN is presented. Firstly, ICN’s main characteristics are summarized, and a short comparison between different ICN architectures is completed. Then, the key details of SDN are highlighted. Moreover, the motivation and benefits of merging ICN with SDN are summarized and the state-of-the-art work on merging ICN and SDN is reviewed and classified from several aspects. Finally, several open research issues are highlighted.

The contributions of this paper are twofold. Firstly, we analyze the decision making problem of caching contents by the network players of Information Centric Networking (ICN) from a game theoretic perspective. We also mention a possible content distribution model for ICN. By our proposed game theory, different network players can find the optimality taking into consideration which benefits them with optimum revenue. Secondly, we present a solution for Live Streaming Media broadcast in ICN and analyze the economic part with a decision tree. We believe, our paper clearly shows the economic incentives for major network players which can stand as a motivation to achieve the faster accommodation of ICN architecture. Besides, we identify some standardization issue in ICN architecture and we emphasize on the need for a common standard for content routers (CR) so that as a node in the ICN, CR ensure scalable content delivery as well as its functionalities match with the Internet open standard philosophy.

It is a paradigm shift in internet architecture from receiver centric model to the information centric networking (ICN) model to improve the end-users' latency experience. With exponential increment in number of users and data volume, efficient usage of the nodes (caches) and content forwarding methodology are key issues in an ICN architecture. In ICN intra-domain routers have storage capacity and they can act as temporary content provider, therefore it reduces average hop count and correspondingly average delay for content download. In this article, we formulate the content management issues in a cache having finite storage capability and propose an efficient content management policy that metamorphoses a node to a self-sustained cache. We address how content packets are stored in buffer of the cache before processing and how they suffer from several finite propagation delays during the processing in a cache. We simulate our proposition with deploying real-life network environment and calculate user experience metrics like average latency, throughput and good-put. From simulation results we observe that our proposed model outperforms the existing state-of-the-art on-path caching policies.

Information-Centric Networking (ICN) is a paradigm shift from host-to-host Internet Protocol (IP)-based communication to content-based communication. In ICN, the content-retrieval process employs names that are given through different naming schemes such as hierarchical, flat, attribute, and hybrid. Among different ICN architectures, Named-Data Networking (NDN) has gained much interest in the research community and is actively being explored for the Internet of Things (IoT) and sensor networks, and follows a hierarchical naming format. NDN protocol follows a pull-based communication model where the content consumer gets content irrespective of the location of the content provider. The content provider in NDN and sensor networks can be considered to be a distributed database that monitors or controls the environment and caches the sensed data or controls information into their memory. The proposed Name-INtegrated Query (NINQ) framework for NDN-based IoT provides a flexible, expressive, and secure query mechanism that supports content retrieval as well as control and configuration command exchange among various nodes in a smart building. Different use cases are presented in this paper that expand on the behavior of proposed query framework in different scenarios. Simulation results of data collection and exchange of control commands show that proposed query framework significantly improves Interest Satisfaction Rate (ISR), Command Satisfaction Rate (CSR), energy efficiency, and average delay. Moreover, it is evident from the simulation results that proposed query framework significantly reduces the number of transmissions in the network in both data collection and exchange of control command scenarios, which improves the network performance.

Information Centric Networking (ICN) is future Internet architecture for distribution of the contents which are location independent and are cached in the ICN nodes, making them efficient for distribution and retrieval. ICN architecture is based on new protocols for unique naming and transferring of data objects over the network. Its focus is on delivery of data objects to the end user rather than communication between client and host. In this paper we have compared different proposed ICN architectures, network caching and challenges of ICN.

Content providers (i.e., entities that own or are licensed to sell or distribute content e.g., BBC, Netflix) are looking for more efficient, secure, cheaper, accountable and scalable mechanisms for the delivery of content to end-users. The Information Centric Networking (ICN) paradigm offers solutions to some of these challenges by decoupling a user's trust in content from where it is obtained by enabling the content to self-verify (i.e., the user can establish integrity, trust and provenance in content received from trusted or untrusted infrastructure). However, there are still associated challenges with using ICN architectures related to content feedback and accountability. In this paper, we propose an ICN architecture extension for content feedback and accountability called the Savant framework, which we apply to the Named Data Networking (NDN) architecture.

Many real-world social networks are decentralized in nature, and the only way to analyze such a network is to collect local views of the social graph from individual participants. Since local views may contain sensitive information, it is often desirable to apply differential privacy in the data collection process, which provides strong and rigorous privacy guarantees. In many practical situations, the local view of a participant contains not only her own connections, but also those of her neighbors, which are private and sensitive for the neighbors, but not directly so for the participant herself. We call such information beyond direct connections an extended local view (ELV), and study two fundamental problems related to ELVs: first, how do we correctly enforce differential privacy for all participants in the presence of ELVs? Second, how can the data collector utilize ELVs to obtain accurate estimates of global graph properties? This paper points out that when collecting ELVs, it is insufficient to apply a straightforward adaptation of local differential privacy (LDP), a commonly used scheme in practice, to protect the privacy of all network participants. The main problem is that an adversarial data collector can accumulate private information on a specific victim from multiple neighbors of the victim; even though the data collected from each neighbor is perturbed under LDP, their aggregate can still violate the victim's privacy. To prevent this attack, we formulate a novel decentralized differential privacy (DDP) scheme, which requires that each participant consider not only her own privacy, but also that of her neighbors involved in her ELV. The stringent privacy requirement of DDP, however, makes it challenging to design an effective mechanism for data collection. Towards this goal, we design a novel multi-phase framework under DDP that enables an analyst to accurately estimate subgraph counts, an important property of social graphs. The main idea is that instead of collecting subgraph counts directly, which would require excessively noise, the analyst first asks individuals about their respective minimum noise scale, which is private information since it depends on the local graph structure, and, thus, must be performed under DDP. For some types of subgraphs, this process is applied recursively, i.e., the analyst asks about the necessary noise to be injected into the private information on the minimum local noise scale required to protect subgraph counts under DDP. As case studies, we instantiate the proposed framework for three common subgraph patterns: triangles, three-hop paths, and k-cliques. Extensive experiments using real data demonstrate that the proposed scheme leads to accurate estimates of global subgraph counts, whereas baseline solutions fail to obtain meaningful result utility.

In this paper, we tackle the problem of answering multi-dimensional range queries under local differential privacy. There are three key technical challenges: capturing the correlations among attrib...

High-dimensional crowdsourced data is pervasive in crowdsensing systems and with the development of IoTs it can produce rich knowledge for the society. However, it also creates serious privacy threats for crowdsourcing participants. To mitigate the privacy concerns in crowdsensing systems, local differential privacy has been derived from the de facto standard of differential privacy in order to achieve strong privacy guaranteed in distributed systems. However, directly achieving local differential privacy on high-dimensional crowdsourced data may lead not only to a prohibitive computational burden but also low data utility. Therefore, in this paper, we propose a local private high-dimensional data publication scheme for crowdsensing systems. In particular, on the participants side, high-dimensional records are locally perturbed to protect privacy, while on the servers side, the probability distribution of original data is recovered by taking advantage of both the expectation maximization algorithm and the theory of the Bayesian network. Extensive experiments on real-world datasets demonstrated the effectiveness of the proposed scheme that can synthesize approximate datasets with local differential privacy.

With the advent of the era of big data, privacy issues have been becoming a hot topic in public. Local differential privacy (LDP) is a state-of-the-art privacy preservation technique that allows to perform big data analysis (e.g., statistical estimation, statistical learning, and data mining) while guaranteeing each individual participant’s privacy. In this paper, we present a comprehensive survey of LDP. We first give an overview on the fundamental knowledge of LDP and its frameworks. We then introduce the mainstream privatization mechanisms and methods in detail from the perspective of frequency oracle and give insights into recent studied on private basic statistical estimation (e.g., frequency estimation and mean estimation) and complex statistical estimation (e.g., multivariate distribution estimation and private estimation over complex data) under LDP. Furthermore, we present current research circumstances on LDP including the private statistical learning/inferencing, private statistical data analysis, privacy amplification techniques for LDP, and some application fields under LDP. Finally, we identify future research directions and open challenges for LDP. This survey can serve as a good reference source for the research of LDP to deal with various privacy-related scenarios to be encountered in practice.

When collecting information, local differential privacy (LDP) alleviates privacy concerns of users because their private information is randomized before being sent it to the central aggregator. LDP imposes large amount of noise as each user executes the randomization independently. To address this issue, recent work introduced an intermediate server with the assumption that this intermediate server does not collude with the aggregator. Under this assumption, less noise can be added to achieve the same privacy guarantee as LDP, thus improving utility for the data collection task. This paper investigates this multiple-party setting of LDP. We analyze the system model and identify potential adversaries. We then make two improvements: a new algorithm that achieves a better privacy-utility tradeoff; and a novel protocol that provides better protection against various attacks. Finally, we perform experiments to compare different methods and demonstrate the benefits of using our proposed method.