Proof of storage-time (PoSt) enforces highly reliable data storage services to data owners by providing lightweight and continuous data availability or possession checks to the uploaded data. Despite being promising, current PoSt schemes mainly focus on static data that will not change after a PoSt process starts. This limitation, however, would largely undermine the practicality of PoSt schemes as they cannot be effectively used for important and emerging cloud storage services like document sharing, code collaboration, and website management that will modify the uploaded data whenever needed. In this paper, we propose DPoSt, a new PoSt system that provides continuous auditing protection to data owners while supporting efficient data updates. DPoSt is built with a tailored suite of cryptographic primitives to update the intermediate auditing proofs while reducing the changes we need to conduct for subsequent challenge and proof pairs in the PoSt process. We develop a prototype of DPoSt, and the evaluation results demonstrate the efficiency of DPoSt for supporting dynamic PoSt operations.

Key-value store is adopted by many applications due to its high performance in processing big data workloads. With the increasing concern for privacy, some privacy-preserving key-value storage systems have been proposed. A remarkable solution is to group key-value pairs into packs and then compress and encrypt each pack separately. The selection of pack size is important for key-value storage systems because it affects both the storage cost in the cloud and the bandwidth cost for data retrieval. However, existing data packing strategies do not consider the trade-off between them. In this paper, we study the optimal compression problem for encrypted key-value stores, aiming to minimize the overall cost of data outsourcing. To solve this problem, we devise an optimal pack size computation scheme, which considers both storage and bandwidth costs. Then, we propose a privacy-preserving key-value storage system. It balances the impact caused by encryption and compression without compromising system performance. Meanwhile, it supports dynamic updates and rich types of queries. Finally, we formally analyze the security of our design. Performance evaluations demonstrate that our proposed pack size computation scheme can minimize the overall cost of data outsourcing, and the designed key-value storage system is feasible in practice.

Although existing schemes improve blockchain throughput by allowing concurrent blocks to be appended to the blockchain, little attention has been devoted to adjusting blockchain throughput dynamically and deduplicating transactions between concurrent blocks. In this article, we propose AdaptChain, an adaptive scaling blockchain with transaction deduplication. When the transaction demand of users in the network is high, the blockchain expands to meet the demand; when the transaction demand is low, the blockchain shrinks to save communication and storage costs. Our transaction deduplication mechanism ensures that no duplicate transactions are added to the blockchain, thereby improving bandwidth utilization and achieving higher effective throughput. Besides, we randomly split the mining power of the system to achieve mining power load balancing and resist attacks. We formally analyze the blockchain security and implement the proposed prototype on Amazon EC2. Experimental results show that AdaptChain achieves dynamic and higher effective blockchain throughput.

Data deduplication at the network edge significantly improves communication efficiency in edge-assisted cloud storage systems. With the increasing concern about data privacy, secure deduplication has been proposed to provide data security while supporting deduplication. Since conventional secure deduplication schemes are mainly based on deterministic encryption, they are vulnerable to frequency analysis attacks. Some recent research has focused on this problem, where several works studied the trade-off between deduplication efficiency and resistance to frequency analysis attacks. However, no existing work can provide different deduplication efficiency and protection for different data chunks. In this paper, we propose a security-aware and efficient data deduplication scheme for edge-assisted cloud storage systems. It not only improves the efficiency of deduplication but also reduces information leakage caused by frequency analysis attacks. In particular, we first define the security level for chunks to measure the security needs of users. Then, we develop an encryption scheme with multiple levels of security for deduplication. It provides higher security protection for chunks with higher security levels, while sacrificing security to achieve higher deduplication efficiency for chunks with lower security levels. We also analyze the security of our proposed scheme. Evaluations on the real-world datasets show the efficiency of our design.

Key-value store is adopted by many applications due to its high performance in processing big data workloads. Recent research on secure cloud storage has shown that even if the data is encrypted, attackers can learn the sensitive information of data by launching access pattern attacks such as frequency analysis. For this issue, some schemes have been proposed to protect encrypted key-value stores against access pattern attacks. However, existing solutions protect access pattern information at the cost of large storage and bandwidth overhead, which is unacceptable for large-scale key-value stores. In this paper, we devise a K-indistinguishable frequency smoothing scheme for encrypted key-value stores, which can resist access pattern attacks launched by passive persistent adversaries with minimal storage and bandwidth overhead. Then, we propose a dynamic K-indistinguishable frequency smoothing scheme. It can efficiently adapt to the changes in access distribution while ensuring the K-indistinguishable security level and bandwidth efficiency. Finally, we formally analyze the security of our design. Extensive experiments demonstrate that our design achieves high throughput while minimizing storage and bandwidth overhead.

Distributed fog computing has received wide attention recently. It enables distributed computing and data management on the network nodes within the close vicinity of IoT devices. An important service of fog-cloud based systems is data deduplication. With the increasing concern of privacy, some privacy-preserving data deduplication schemes have been proposed. However, they cannot support lossless deduplication of encrypted similar data in the fog-cloud network. Meanwhile, no existing design can protect message equality information while resisting brute-force and frequency analysis attacks. In this paper, we propose a privacy-preserving and compression-based data deduplication system under the fog-cloud network, which supports lossless deduplication of similar data in the encrypted domain. Specifically, we first use the generalized deduplication technique and cryptographic primitives to implement secure deduplication over similar data. Then, we devise a two-level deduplication protocol that can perform secure and efficient deduplication at distributed fog nodes and the cloud. The proposed system can not only resist brute-force and frequency analysis attacks but also ensure that only the data operator can capture the message equality information. We formally analyze the security of our design. Performance evaluations demonstrate that our proposed design is efficient in computing, storage, and communication.

Edge servers are densely deployed in the future mobile edge networks to meet the rapid increasing demand of mobile users. Since the distribution and traffic demand of user equipment (UE) fluctuate in time and over space, a number of edge servers may be underutilized which causes a great deal of energy waste. Therefore, we intend to reduce the energy cost of mobile edge networks, by dynamically switching on/off edge servers according to the variation of UEs' distribution. We formulate the energy saving problem in mobile edge networks as the minimum energy consumption (MinEn) problem which involves two critical issues: (1) cooperative service caching and UE association of adjacent BSs; (2) switching on/off edge servers. To solve the MinEn problem, we propose a dynamic server switching algorithm along with a lightweight UE distribution prediction mechanism. Simulation results show that our algorithm can greatly reduce the energy consumption of mobile edge networks compared with existing methods.

Mobile edge computing (MEC) can improve the quality of services and save the bandwidth of backhual networks, by placing application services in the base stations (BSs), which are endowed with computing resources and are in close proximity to user equipments (UEs). Since the capacity of an individual BS is limited, only a small number of service instances can be allowed for each BS at the same time. Meanwhile, in a densely deployed network, the coverage areas of adjacent BSs are overlapped. Therefore, these capacity-limited BSs can collaboratively optimize their service placements to improve the performance of MEC. In this paper, we investigate the collaborative service placement (CSP) problem in MEC, which aims to minimize the traffic load caused by service request forwarding. The CSP problem involves several difficult issues, including correlations of adjacent BSs' service placement decisions, joint service placement and UE association, and joint allocation of computing and radio resources. This makes the CSP problem be a complex combinatorial optimization problem. To solve the CSP problem, we propose an efficient decentralized algorithm based on the Matching Theory. It can optimize the decisions of service placement and BS-UE association for BSs, according to local interactions between BSs and UEs. Our proposed algorithm is practical for large-size networks, and its effectiveness is demonstrated by the simulation results.

Caching application services at the edge of mobile networks can both reduce the traffic load in core networks and improve the quality of services. Since the capacity of a single BS is constrained, only a small number of service can be executed simultaneously by each BS. However, when the BSs are densely deployed in the network, the BSs that are close to each other can cooperatively cache the services to improve the performance of the system. Moreover, we should avoid frequent service switching when the users' service requests always change. In this paper, we study the dynamic service caching (DSC) problem in mobile edge networks. Our objective is to minimize the traffic load that needs to be forwarded to the cloud, as well as considering service switching cost of BSs. This DSC problem involves two important issues, which include cooperative service caching of adjacent BSs and service switching in adjacent time slots. To solve the DSC problem, we propose a dynamic service caching algorithm for the BSs to cooperatively cache the services in an online manner. The simulation results show that our algorithm can greatly reduce the forwarded traffic load without frequently changing the service caching of BSs.