• <tr id="yyy80"></tr>
  • <sup id="yyy80"></sup>
  • <tfoot id="yyy80"><noscript id="yyy80"></noscript></tfoot>
  • 99热精品在线国产_美女午夜性视频免费_国产精品国产高清国产av_av欧美777_自拍偷自拍亚洲精品老妇_亚洲熟女精品中文字幕_www日本黄色视频网_国产精品野战在线观看 ?

    Double-Blockchain Assisted Secure and Anonymous Data Aggregation for Fog-Enabled Smart Grid

    2022-04-24 03:23:14SiguangChnLiYangChuanxinZhaoVijayakumarVaraarajanKunWang
    Engineering 2022年1期

    Siguang Chn*, Li Yang Chuanxin Zhao, Vijayakumar Varaarajan, Kun Wang

    a Jiangsu Key Lab of Broadband Wireless Communication and Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210003, China

    b Jiangsu Engineering Research Center of Communication and Network Technology, Nanjing University of Posts and Telecommunications, Nanjing 210003, China

    c Anhui Provincial Key Laboratory of Network and Information Security, Anhui Normal University, Wuhu 241000, China

    d School of Computer Science and Engineering, Vellore Institute of Technology, Chennai 600127, India

    e Department of Electrical and Computer Engineering, University of California, Los Angeles, CA 90095, USA

    Keywords:Blockchain Fog computing Homomorphic encryption Smart grid Anonymity

    ABSTRACT As a future energy system, the smart grid is designed to improve the efficiency of traditional power systems while providing more stable and reliable services.However,this efficient and reliable service relies on collecting and analyzing users’ electricity consumption data frequently, which induces various security and privacy threats.To address these challenges,we propose a double-blockchain assisted secure and anonymous data aggregation scheme for fog-enabled smart grid named DA-SADA.Specifically,we design a three-tier architecture-based data aggregation framework by integrating fog computing and the blockchain, which provides strong support for achieving efficient and secure data collection in smart grids.Subsequently,we develop a secure and anonymous data aggregation mechanism with low computational overhead by jointly leveraging the Paillier encryption, batch aggregation signature and anonymous authentication. In particular, the system achieves fine-grained data aggregation and provides effective support for power dispatching and price adjustment by the designed double-blockchain and two-level data aggregation. Finally, the superiority of the proposed scheme is illustrated by a series of security and computation cost analyses.

    1. Introduction

    Smart grids, as next-generation power networks, provide efficient and intelligent electricity and information exchange to maximize energy usage efficiency and meet modern demands by integrating advanced information processing and communication technologies [1,2]. For example, the smart meter in a user’s home can sense the electricity usage information of home appliances in real time,and the control center can collect and analyze these data to learn the user’s power usage behaviors and provide dynamic pricing and flexible power dispatching policies [3–5]. However,the smart grid is confronted with substantial data communication and computation burdens given the explosive growth of smart meters [6,7]. Furthermore, the exposure of the collected power consumption data of smart meters promotes privacy leakage risks because the power consumption data can be used to explore users’living habits and even infer their economic status [8]. In addition,tampering and forgery attacks will also produce a great threat to the stability of the smart grid [9,10]. For example, the false data injection attack from a cyber attacker caused the world-shaking Ukraine blackout accident in 2015[11].To address the above challenges of performance, privacy, and security in the smart grid,many research schemes were proposed, among which a typical representative, that is a secure and efficient data aggregation mechanism, has attracted appreciable attention for its significant advantage. Currently, the smart grid data aggregation schemes can be roughly divided into the following three categories.

    The first category is composed of the data aggregation scheme with the traditional network architecture. For example, Lu et al.[12] presented an efficient and privacy-preserving data aggregation mechanism by integrating the superincreasing sequence,homomorphic Paillier encryption,and batch verification,achieving efficient multidimensional data aggregation with security and privacy protections. Furthermore, Ni et al. [13] constructed a security-enhanced data aggregation scheme by jointly using homomorphic encryption, the trapdoor hash function, and homomorphic authenticators, thereby improving the computation and communication costs of work with confidentiality and integrity guarantees. From the perspective of dynamic pricing and service support, Gope and Sikdar [14]formulated a privacy-friendly lightweight data aggregation mechanism.It realizes strong privacy protection under dynamic billing, which is especially suitable for devices with limited computing resources. Without the support of a trusted third party, Liu et al. [15] proposed a practical data aggregation scheme with efficient privacy preservation.In the proposed scheme,the trusted users are linked to form a virtual aggregated area, and the aggregated results are used for data analysis,such that the user’s personal privacy is protected and the robustness of the system is improved. From the perspective of finegrained aggregation, Li et al. [16] developed a multisubset data aggregation scheme with efficient privacy preservation. According to the different ranges of the power consumption data, it can achieve multisubset aggregation and provide fine-grained data service; at the same time, the user’s privacy is preserved with a low computation cost. Although the developed schemes in the above literature achieve efficient and secure data aggregation, further opportunities exist to reduce the data processing delay and communication overhead that are due to the weakness of the employed traditional network architecture.

    Fortunately, fog computing, as a promising computing paradigm, has been developed to overcome the weakness of the traditional network architecture and has been proven to decrease the delay and communication overhead significantly, especially when combined with cloud computing [17]. Consequently, the second category of solutions developed the data aggregation mechanism with the edge/fog computing architecture. For example, Lu et al. [18]constructed a fog-assisted privacy-preserving data aggregation scheme by integrating the Paillier encryption, the one-way hash chain, and Chinese remainder theorem. This scheme has the property of aggregating the data of hybrid Internet of Things(IoTs)into one, and possesses a filtering function for fake data. Based on the application demands of different data types, Huang et al. [19]studied a fog-enabled selective data aggregation scheme that also considers the reliability and privacy-preserving problems. To further enhance the privacy effect of the above methods, Lyu et al.[20] proposed a fog-based differential privacy-preserving data aggregation scheme; this scheme achieves differential privacy for statistical data and ensures the data confidentiality from the aggregator.From the resource-constraint consideration in an edge computing system, Zhang et al. [21] presented an efficiency-enhanced privacy-preserving data aggregation scheme by transferring the time-consuming signature operations offline, thereby effectively relieving the online computation burden. Focusing on anonymous authentication in the fog-enabled smart grid, Zhu et al. [22] conceived an anonymous data aggregation scheme by employing the Paillier cryptosystem and blind signature, which can provide strong privacy protection with low computation and communication costs. Although the above solution reduces the system delay and communication overheads significantly, and provides privacy and security protection to some degree, this category of schemes still faces issues of security and centralization. For example, when a user’s private information is transmitted to a fog node, and a malicious attacker successfully intercepts the channel and steals the secret key, it is difficult to guarantee the privacy of the user.Moreover,all of the users’data are concentrated in the fog or cloud layer, which inevitably introduces the problem of centralization.

    The emergence of the blockchain technique[23]has provided a new perspective to address the above problems because of its decentralization and nontampering features. Currently, there are several studies that have applied the blockchain to the smart grid.For example, in Ref, [24], Liang et al. investigated a blockchainbased data protection scheme for the smart grid,and it proves that the blockchain can effectively improve system security under cyber-attacks. Therefore, the third category of solutions encompasses the combination of data aggregation and the blockchain technique.Specifically,Fan and Zhang[25]proposed a secure data aggregation for smart power regulation by integrating the consortium blockchain into the smart grid, in which a multireceiver model for collecting multidimensional data is developed, and based on smart contracts,it establishes flexible power monitoring and management mechanisms to enhance the security of the smart grid. Guan et al. [26] studied a blockchain-assisted anonymous data aggregation scheme for the smart grid;it enhances the system security and obtains better performance compared with other solutions. However, the users’ power consumption data are transmitted in plaintext form in groups and will be confronted with some security risks. Although the above blockchain-based privacy-preserving data aggregation schemes effectively enhance the smart grid security and solve the problem of centralization and single point failure,all of them do not consider the edge computing paradigm, causing an ineffective utilization of local resources. As a result, the system efficiency has a large space for improvement. Accordingly, the works [27] and [28] were developed to improve system performance by combining the blockchain and edge computing,but these two schemes do not provide specific executable solutions.

    The above schemes solve the corresponding problems of the smart grid to varying degrees,but there are still many weaknesses.Different from the existing solutions, we propose a doubleblockchain assisted secure and anonymous data aggregation(DA-SADA) scheme for the fog-enabled smart grid by integrating the blockchain, the Paillier cryptosystem, batch verification, and an anonymous authentication mechanism. Specifically, the main contributions of this scheme are summarized as follows:

    (1) We design a three-tier architecture-based data aggregation framework by integrating fog computing and the blockchain. It is a security-enhanced framework, and the local resources are exploited effectively, which provides strong support for achieving efficient and secure data collection in the smart grid.

    (2) We develop a secure and anonymous data aggregation mechanism with low computational overhead by jointly leveraging the Paillier encryption, batch aggregation signature, and anonymous authentication. It can effectively resist various security threats (such as eavesdropping, tampering, and replay attacks)and provide multiple privacy preservations.

    (3)The system achieves fine-grained data aggregation and provides effective support for power dispatching and price adjustment by the designed double-blockchain and two-level data aggregation.Additionally, this design further strengthens the system security and robustness.

    The remaining parts of this paper are organized as follows. In Section 2, we describe some preliminaries. Section 3 introduces the constructed network model in detail. Our proposed scheme is presented in Section 4, followed by the security and performance evaluation in Section 5. Section 6 ultimately draws the conclusion of this paper.

    2. Preliminaries

    2.1. Blockchain

    The blockchain can be considered as a peer to peer (P2P) distributed database that creates blocks and links in chronological order [29], which is designed to provide decentralized and distributed solutions for a wide range IoT and industrial Internet of Things (IIoT) applications. The main blockchain components include transactions,blocks,smart contracts,the consensus mechanism, cryptography, and the P2P network [30]. Specifically, in a blockchain network, the participants act as the distributed nodes for protecting and maintaining the shared record of transactions collaboratively; it does not need any trusted party for supervision and management.All nodes are responsible for sharing,packaging,verifying,and storing new transactions generated in the blockchain network.Therefore,it can establish trust among participating entities that do not trust each other in a distributed scenario.It also has decentralization, nontampering, and security features.

    Decentralization: The distributed structure of the blockchain ensures the decentralization property. Furthermore, the thirdparty maintenance management is not required, and the nodes in the network are completely autonomous based on the incentive mechanism.

    Nontampering: Nontampering means that once transaction data are recorded in the blockchain, the record cannot be successfully tampered with or deleted.

    Security:The data written to the blockchain needs to be collectively verified, which indicates that successful tampering needs at least 51% of the computing power in the entire network, which is usually impossible in practice.

    2.2. Paillier encryption

    The Paillier homomorphic encryption method is widely used in the privacy protection area. It can directly operate on ciphertext,thus effectively protecting data privacy. Specifically, the Paillier encryption is an additive homomorphic encryption,and it consists of key generation,encryption operation,and decryption operation.

    2.3. Bloom filter

    The Bloom filter consists of a long binary vector and a series of random mapping functions;it has the advantages of low computational complexity, high space utilization, and query efficiency. It can quickly confirm whether an element exists in the set.

    We assume that there are k hash functions {h1,h2,...,hk} and one set with elements {x1,x2,...,xω}. These elements are mapped to the corresponding position of the Bloom filter by k uniformly independent hash functions, and the value of the corresponding position is set to 1. The specific operation is shown in Fig. 1.

    Element adding: As shown in Fig. 1, we hash the element by k times to obtain k hash values {h1(x1),h2(x1),...,hk(x1)}, and then based on these values, find the corresponding positions of the Bloom filter. Finally, let values k of the corresponding positions in the Bloom filter be 1.

    Fig. 1. Generation of the bloom filter.

    Element query: To query whether the element x1exists in the Bloom filter, we first calculate k hash values of the element x1,which is denoted as {h1(x1),h2(x1),...,hk(x1)}, and then check whether the values of the corresponding positions in the Bloom filter are all 1.If one of them is zero,it indicates that the element x1is not stored in the Bloom filter;otherwise,the element x1is stored in the Bloom filter.

    3. Network model and threats

    3.1. Network model

    In our constructed network model,a fog-enabled data aggregation smart grid consists of four entities (smart meters, fog nodes,cloud server, and trust authority (TA)) and is displayed in Fig. 2.Specifically, we assume that the coverage area of a smart grid is divided into m subareas,and each subarea deploys n smart meters for sensing user’s power consumption information. All of the m·n smart meters form the user layer. Accordingly, each subarea deploys a fog node to collect and aggregate the data from its own area, and all the m fog nodes form the fog computing layer that is located at the edge of the network between the user and service supporting layers.At the service supporting layer,the cloud server is used to process the data uploaded from the fog layer and generate real-time decision-making. TA is responsible for the generation of the entire system’s parameters. The specific function definitions of these entities in each layer are presented in detail in the following part.

    The User layer: The user layer is mainly composed of a large number of smart meters. For example, in the subarea j, the ith smart meter SMijobserves a user’s real-time power consumption,and then encrypts and signs these consumption data.Next,it sends these encrypted data to the aggregation node at the user layer.The aggregation node aggregates the verified ciphertext to generate the first-level aggregation ciphertext, and then encapsulates the related information into a block.At the same time,the newly generated block will be added to the user aggregation(UA)-blockchain by the consensus mechanism. In these processing processes, the identity of SMij(i.e., the user) always exists under a pseudonym.Finally, the generated UA-blockchain is sent to the fogjfor further processing.

    Fog computing layer: The fog computing layer is the middle layer between the user and service supporting layers that is deployed at the edge of the network, which enables the secondlevel aggregation of the encryption data to significantly reduce the communication overhead. Specifically, when the fogjreceives the first-level aggregated ciphertext from the UA-chain sent by the aggregation node in the user layer, it signs the aggregated ciphertext and sends it to the aggregation node at the fog layer for secondary aggregation. Next, the aggregation node encapsulates the related information into a new block,and then the newly generated block is added to the fog aggregation(FA)-blockchain by the consensus mechanism. Finally, the generated FA-chain is sent to the cloud server.

    Fig. 2. Network architecture of the developed DA-SADA. UA: user aggregation; FA: fog aggregation.

    Service supporting layer: In this layer, the cloud server can record,analyze,store,and manage users’power usage information in real time, which is automatically executed by a smart contract,so the whole process does not need human intervention, improving the efficiency of the system and enhancing the security of the privacy data. Specifically, when the cloud server obtains the second-level aggregated ciphertext from the FA-chain that is sent by the aggregation node at the fog layer,it performs the decryption operation to recover the plaintext of the second-level aggregation result,and then utilizes Horner’s law to achieve fine-grained aggregation plaintext. The combination of coarse and fine-grained aggregation results provides support of diverse data for effective power dispatching management.

    TA:TA is primarily responsible for generating and managing all public parameters and secret keys for entities in the system.Meanwhile,it creates a Bloom filter for smart meters of each subarea by collecting a user’s pseudonym.This Bloom filter will be sent to the corresponding users. The same operation is adaptable to the fog layer.

    3.2. Adversary model

    In the smart grid scenario, in order to pry into a user’s private affairs,an eavesdropper may exist that can eavesdrop on the communication links between smart meters and fog nodes.At the same time, the active attacker may tamper with the transmission information and launch replay attacks to threaten the security of the smart grid. In our adversary model, we divide threats that may occur in the network into internal and external attacks.

    Internal attack: The first category of internal attacks is composed of the malicious node attacks,which occur during the generation of the blockchain in the user and fog computing layers. For example, in the generation process of the blockchain, a malicious node pretends to be a legal node in the network,and initiates some active attacks (e.g., tampering, forgery, replay) to impair the authenticity and integrity of the user’s private data. Therefore,the system should have the capability of identifying the legality of node identities in the consensus process. The second category of internal attacks is described as honest-but-curious in terms of fog and cloud nodes. For example, the fog node may be affected by undetected malware, and malware will eavesdrop on the data from devices, so we must ensure that the fog node does not observe the user’s private data throughout the process. Similarly,the system should guarantee that the user’s personal private data cannot be derived from the cloud server.

    External attack: The attacker can eavesdrop and tamper with the transmitted data over communication links; it also can launch a replay attack. Therefore, the system must ensure that the attacker cannot successfully obtain the privacy information over the communication links and that it is immune to active attacks.

    4. Double-blockchain assisted secure and anonymous data aggregation

    In this section, we develop a DA-SADA scheme for the fog-enabled smart grid by integrating the blockchain, the Paillier cryptosystem, batch aggregation verification, and an anonymous authentication mechanism.It consists of four parts:system initialization, UA-blockchain generation, FA-blockchain generation, and service supporting.

    4.1. System initialization

    In our network scenario, the trusted third party TA is responsible for the system initialization, where there are three procedures that need to be executed in this system initialization process, that is,the generation of system parameters,the distribution of system parameters, and the generation of the Bloom filter.

    The generation of system parameters:In the generation stage of system parameters,the TA selects the system security parameter κ to calculate two safe large primes |p|=|q|=κ. Consequently, it calculates N =pq as the public key of the homomorphic encryption algorithm and λ=lcm(p-1, q-1) as the corresponding private key.Meanwhile,the system randomly selects r ∈Z*Nand calculates s=rNmod N2. Let g =N+1 and define the function as

    Furthermore, for the sake of providing identity anonymity, the SMijchooses a random prime number Xijto calculate its secret key Yij=X-1ijmod N2; this public key Xijis used to calculate the smart meter’s pseudonym, that is, Pseuij=Xijmod N2. Similarly,the fog node fogjchooses a random prime number Xjas its public key and calculates Yj=X-1jmod N2as its secret key to denote the fog device’s pseudonym Pseuj=Xjmod N2.Finally,the TA chooses the secure cryptographic hash function H:{ 0,1}*→Z*N.

    The distribution of system parameters: With the generation of all system parameters (λ,N,s,H,Xij,Xj,Yij,Yj), the public parameters(N,H)will be published online and the remainder of them will be allocated to the corresponding real entities. Specifically, keys(Xij, Yij, s), (Xj,Yj), and λ are assigned, respectively, to the SMij,fog node fogjand cloud server through the secret channel.

    4.2. Generation of UA-blockchain

    By considering the privacy leaks from the data analysis of the power consumption and tampering threat, the sensing device(i.e., smart meter) needs to encrypt the power consumption data of the user, and the relevant information needs to be digitally signed for integrity. This process is called transaction generation.Subsequently,the aggregation node aggregates the encrypted data and records the corresponding information into a block.Finally,the aggregation node generates the UA-blockchain by the consensus mechanism. The specific generation process of the UA-blockchain is shown in Fig. 3 and is represented below.

    4.2.1. Transaction generation

    The generation of power consumption ciphertext:For a subarea with n smart meters, in a certain time slot ts, we denote the data item of the SMijas dij;then,each smart meter calculates ciphertext Cijby the following formula:

    where 1 ≤i ≤n, 1 ≤j ≤m. We calculate g =N+1 and obtain another form of the Paillier encryption algorithm c=(1+mN)rNmod N2according to the nature of (1+N)m≡(1+mN)mod N2,which is mainly used to avoid the cumbersome calculation in the encryption and decryption operation,thereby reducing the computational overhead.

    Fig. 3. Generation of UA-blockchain. This process includes three steps: transaction generation, creation of new block, and blockchain generation.

    where this calculation process implies that once the block is added into a chain,it is difficult to tamper with the block content since the hash value of the previous block is involved in calculating the hash value of the current block.

    4.2.3. Blockchain generation

    After the aggregation node creates a new block,the new block is broadcast in this subarea. The ordinary node in this subarea verifies records in this new block,and each node only verifies the data related to itself for meeting the real-time scheduling requirement in the smart grid.If it is consistent with the original data,it passes the verification and broadcasts the verification result to other nodes in the user layer. After collecting the correctness confirmation message sent by the other 2n/3+1 nodes or more,this new block is considered to be valid and added to the UA-blockchain. In our blockchain network, we assume that the number of malicious nodes should be less than 1/3 of the total number of network nodes. Because we define that a new block can only be added to the blockchain when it passes the verifications of 2n/3+1 nodes or more, we set such threshold value for security consideration. It also implies the attacker can tamper the information in the block successfully only when it captures more than 2/3 nodes of the network. The specific consensus process is shown in Fig. 3.

    4.3. Generation of FA-blockchain

    Similarly, in the fog computing layer, the generation of the FAblockchain consists of the transaction generation, new block creation, and blockchain generation.

    4.3.1. Transaction generation

    The transaction generation of the fog layer is similar to that in the user layer. First, when the fog node j receives encrypted data from the UA-blockchain, these encrypted data will be digitally signed for integrity at fog node j. Then, the selected aggregation node at the fog layer performs the aggregation operation for all of the Cj,j ∈{1,2,...,m},that is,it obtains the secondary aggregation result. Similarly, we choose the fog node with the largest remaining computing resource as the aggregation node.

    The generation of the signature: When the jth fog node fogjreceives the aggregated power consumption ciphertext Cjof the corresponding subarea, it can calculate the signature σj:

    After the successful verification of the smart meters’signatures,the aggregation node performs an aggregation operation to obtain the secondary aggregation ciphertext CASfor all subareas.

    4.3.3. Blockchain generation

    After the aggregation node creates a new block in the fog computing layer, the new block is broadcast to other fog nodes and added into the FA-blockchain through the consensus mechanism.The consensus mechanism is similar to that of the user layer.First,the ordinary node in the fog computing layer verifies the records in this new block and each node only verifies the data related to itself.If it is consistent with the original data, it passes the verification and broadcasts the verification result to other nodes in the fog computing layer. After collecting the correctness confirmation message sent by the other 2m/3+1 fog nodes or more, this block is considered to be valid and added to the FA-blockchain.

    4.4. Service supporting

    When the cloud server receives the FA-blockchain from the fog computing layer,it reads the secondary aggregation ciphertext and decrypts the ciphertext by using the Paillier decryption algorithm.To leverage the Paillier decryption algorithm effectively,we further specify the components of Eq. (13), that is, Eq. (13) can be rewritten as

    Due to the values of these coefficients, it achieves the finegrained aggregation successfully, that is, it not only obtains the entire power consumption of the network but also recovers the subarea’s data.

    Algorithm 1. Horner rule-based analytical algorithm.Input:M and R.Output:Total power consumption UAj in each subarea j,j=1,2,...,m.1: Begin 2:x0 ←M/R, a1 =R1,a2 =R2,...,am =Rm;x0 =UA1+R1UA2+···+Rm-1UAm;3:For j ←1 to m do 4:UAj ←xj-1 mod R;5:xj ←xj-1 mod R;6:End for 7:Obtain (UA1,UA2,...,UAm).8: End

    Once the cloud server gains the power consumption of each subarea through the above operations, these fine-grained data can be explored to predict the power usage trend of each subarea,and then provide decision support for power dispatching and price adjustment. Accordingly, the smart contract enables these decisions to be executed automatically and develops the time-of-use pricing feedback strategy to encourage users to adjust their electricity use habits for alleviating the burden of the power grid and improving the power utilization efficiency.

    With the accumulation of data, the blockchain sharing ledger will become increasingly larger, which is called blockchain bloat.For example, in the past nine years, the size of the Bitcoin system ledger has reached 153.1 GB[32].All historical transaction items of Bitcoin need to be kept for a long time because they are used to calculate account balances.For the proposed aggregation mechanism in this paper, the smart meter’s data item of the new generation does not rely on the previous one, thus there is no need to save all the data items on each node.We recommend regularly cleaning out obsolete data items and releasing storage space in the relevant nodes.

    5. Security and performance evaluations

    In this section, we will discuss the security and anonymity properties of the proposed scheme, and analyze the performance in terms of the computation cost.In particular,we perform a quantitative analysis on the successful probability of tampering attacks under different scenarios, which proves the high security of our proposed scheme.Furthermore,the computation costs of the identity authentication and whole system are given in detail, and they show that the proposed scheme is lightweight and more suitable for systems with real-time requirements.

    5.1. Security analysis

    Identity anonymity and authenticity: The user identity is usually associated with the private information, and the disclosure of the user identity information can often cause a series of hazards.In the proposed scheme in this paper, the identities of smart meters and fog devices always exist in a pseudonym form,that is, Pseuij=Xijmod N2and Pseuj=Xjmod N2, respectively,where the public keys Xijand Xjare randomly selected by the user and fog device,respectively, and the generated pseudonyms Pseuijand Pseujare random and are not associated with the true identity of the user and fog device. Even if the malicious attacker decrypts the meter’s data of users successfully, it still means nothing because it cannot obtain the real identity of the user. Thus, our scheme realizes the anonymity of the user identity. At the same time, an illegal node may exist that attempts to impersonate the legal user’s identity; however, our identity authenticity mechanism can identify this identity fraud behavior since we have already collected the legal pseudonym in advance and mapped it in the Bloom filter. It can quickly determine whether the node’s pseudonym is in the Bloom filter by the querying operation.

    5.2. Successful attacking probabilities

    According to the threat model definition in Section 3,we choose two typical attacks to evaluate their impacts on aggregation results, that is, tampering attacks in nodes and over links. To demonstrate the advantages of our proposed solution,we comparatively analyze the successful probability of tampering attacks under different solutions.

    5.2.1. Tampering attack in nodes

    In our threat model,we assume that the total number of smart meters that attackers need to manipulate is w if they want to successfully launch a tampering attack, and the total number that attackers need to manipulate of fog nodes is f. To make it easier to understand, we suppose that the compromised probability of each smart meter is independent and denoted as αi, where i=1,2, ...,w,...,nm and 0 ≤αi≤1. Similarly, the compromised probabilities of the fog node and cloud server are represented,respectively, by βj, j=1,2,...,f,...,m, 0 ≤βj≤1 and γ. Meanwhile,we assume the intercepted probability of the smart meter’s secret key by a malicious node is independent and set to be ?i,where i=1,2,...,w,...,nm and 0 ≤?i≤1.

    Therefore, the successful probability of tampering attack under the traditional secure scheme can be given as

    5.2.2. Tampering attack over links

    In this part,we consider the attack that intercepts or forges data packets over the communication channels.

    where the weight is 1/2, indicating that the attacker chooses to attack the two kinds of communication links equally.

    where the weight is 1/2,indicating that the successful probability of an attacker launching two kinds of attacks is independent and equal.

    5.2.3. Successful probabilities

    In the previous two parts,we analyzed the successful probability of the tampering attack for the traditional and our proposed schemes from a theoretical perspective. To show the analysis results more intuitively, we use the Monte Carlo simulation method to further analyze the successful probability. In this simulation scenario, we assume that there are 20 smart meters in each subarea and 1 cloud server in the service supporting layer,and the number of fog nodes is 50.Then,we assume that the probability that attackers need to manipulate smart meters is 10% to 100%; thus, the w is variable from 100 to 1000 in the entire network. Meanwhile, we define that the range of variables α,β,?,η and η all vary from 0.9 to 1, and the range of γ is set to be [0, 0.1]. The values of variables α,β,?,η and η are randomly selected within their ranges, and we execute the experiment 1000 times to evaluate the average value of the simulation results.The experiment runs on a notebook with an Intel Core i5-7200U CPU @ 2.50 GHZ, with 8.00 GB RAM.

    Fig.4 depicts the interrelation between the successful attacking probability and the total number of smart meters that attackers need to manipulate. Notably, the successful probability exhibits a continuous decline with the increase of the number of the manipulated smart meters,and our proposed scheme demonstrates a significant advantage in the reduction of security threats. In particular, the successful attacking probability approaches 0 in our scheme when the total number that attackers need to manipulate is more than 500. The main reason for this result is that our proposed scheme designs two consensus mechanisms in the generation of the UA-blockchain and FA-blockchain,and the consensus mechanism needs group verification. Therefore, the use of the double-blockchain significantly enhances the robustness of the system.

    5.3. Computation cost

    In this subsection,we analyze the computation costs of identity authentication and the entire system. In the simulation scenario,we assume that the number of fog nodes is variable from 5 to 50. Meanwhile, we set the error probability of the Bloom filter to 0.01, and define the RSA modulus N and parameter p as 1024 bits and 160 bits,respectively.Although the content-based Bloom filter usually has conflicts, the conflict probability is very small. For example, in the case of using seven different hash functions, to use a bit string of 2 MB size, the overall error rate is less than 0.01. Therefore, it is reasonable to set the error probability of the Bloom filter to 0.01. For convenience of explanation, we denote TE1,TE2,TMand TPas the exponentiation operations in Z*N2, the exponential operations in G, the multiplication operations and the bilinear pairing in G, respectively. We use the pairing-based cryptography (PBC) library to implement these operations. The data set of simulation is from Commission for Energy Regulation Ireland [34]. Table 1 lists the operation notations and their time costs in the evaluation process.

    Fig. 4. Successful attacking probabilities under different solutions.

    Table 1 Operation notations and time costs.

    Fig. 5 shows the time cost of identity authentication with and without the Bloom filter. Observing from this figure, we can find that the time cost of the traditional scheme without the Bloom filter grows sharply with the increase of the number of smart meters,but our proposed scheme has a limited increasing range and the time cost is much lower than the traditional scheme. This is because the Bloom filter uses multiple hash functions to improve space utilization, which greatly improves the query efficiency of the authentication process.

    Subsequently, for the sake of comprehensively displaying the computation cost, we analyze the computation cost of the entire system with our developed scheme, and conduct a comparison with two benchmark schemes, that is, the security-enhanced data aggregation scheme (SEDA) [13] and a lightweight privacypreserving data aggregation scheme for edge computing (LPDAEC) [21]. Because the computation cost of the hash operation is negligible compared with exponentiation and multiplication operations, we do not consider the cost of the hash operation in our evaluation process.

    Fig. 5. Time cost of identity authentication.

    In Fig.6,similar to the computation cost of identity authentication,the total computation cost of the system is proportional to the number of smart meters.Meanwhile,we can observe that our proposed scheme achieves a significant reduction in the total computation cost compared with SEDA and LPDA-EC. For example, when the number of smart meters is 500, the total computation cost of our proposed scheme is 103ms, which reduces by 80% and 60%that of SEDA and LPDA-EC, respectively. Furthermore, the reduction of the computation cost will become more pronounced with the increase of the number of smart meters.This is mainly because the required time for bilinear pairing is much larger than that of other operations, and both SEDA and LPDA-EC include the expensive bilinear pairing operation during the verification process.However, in our proposed scheme, the use of the pairing calculation is effectively avoided,which significantly reduces the computation cost at the same time.

    From the above security and performance analysis results, we can conclude that the proposed security and anonymous data aggregation scheme significantly reduces the system computationcost while providing strong security and anonymity protections.Moreover, it is more suitable for systems with real-time highfrequency data collection and aggregation requirements in the smart grid.

    Table 2 Time costs.

    Fig. 6. Total computation cost of the system.

    6. Conclusions

    The smart grid can achieve reliable and stable services by collecting and analyzing the users’ electricity consumption data, but the users’security and privacy are usually threatened during these operations.Therefore,we propose a DA-SADA scheme.Specifically,we construct a security-enhanced three-tier architecture by combining fog computing and the blockchain, and the local resources are exploited effectively.Subsequently,a lightweight secure aggregation mechanism is developed to ensure the confidentiality,integrity, and authenticity of private data. In particular, in order to realize the flexible regulation of power, we design the doubleblockchain to achieve fine-grained aggregation of the users’power consumption data, and the double-consensus in the formation of the double-blockchain further enhances the security of the system.Finally,the security analysis confirms the high security of our proposed scheme, and the comparison analysis of computation costs in the entire system further validates its performance advantage,providing a more suitable solution for systems with real-time requirements.Although our proposed scheme provides an efficient and secure data collection mechanism for smart grid, it still lacks an efficient and smart method to select aggregation node. Therefore, in future work, we plan to develop a dynamic and smart aggregation node selection mechanism to improve the applicableness of developed scheme in the real network scenario by integrating machine learning method.

    Acknowledgments

    This work was partially supported by the National Natural Science Foundation of China (61971235, 61871412, and 61771258), the Six Talented Eminence Foundation of Jiangsu Province (XYDXXJS-044), the China Postdoctoral Science Foundation(2018M630590), the 333 High-level Talents Training Project of Jiangsu Province, the 1311 Talents Plan of Nanjing University of Posts and Telecommunications (NUPT), the Open Research Fund of Jiangsu Engineering Research Center of Communication and Network Technology, NUPT (JSGCZX17011), the Scientific Research Foundation of NUPT (NY218058), and the Open Research Fund of Anhui Provincial Key Laboratory of Network and Information Security (AHNIS2020001).

    Compliance with ethics guidelines

    Siguang Chen,Li Yang,Chuanxin Zhao,Vijayakumar Varadarajan,and Kun Wang declare that they have no conflict of interest or financial conflicts to disclose.

    日韩熟女老妇一区二区性免费视频| 日本欧美视频一区| 交换朋友夫妻互换小说| av国产精品久久久久影院| 日韩免费高清中文字幕av| 欧美xxⅹ黑人| 男女午夜视频在线观看| 久久久久人妻精品一区果冻| 一级毛片黄色毛片免费观看视频| 日韩中文字幕欧美一区二区 | 久久97久久精品| 男女边摸边吃奶| 如日韩欧美国产精品一区二区三区| 中文乱码字字幕精品一区二区三区| √禁漫天堂资源中文www| 亚洲av欧美aⅴ国产| 国产欧美日韩一区二区三区在线| 亚洲av电影在线观看一区二区三区| 国产福利在线免费观看视频| 成年女人在线观看亚洲视频| 久久综合国产亚洲精品| 亚洲三区欧美一区| 国产男人的电影天堂91| 99国产精品免费福利视频| 色哟哟·www| 国产男女超爽视频在线观看| 婷婷色综合www| 国产 一区精品| 在线观看一区二区三区激情| 久久韩国三级中文字幕| 免费观看a级毛片全部| 精品酒店卫生间| 三上悠亚av全集在线观看| 夫妻午夜视频| 亚洲av日韩在线播放| 十八禁网站网址无遮挡| 午夜福利一区二区在线看| 亚洲精华国产精华液的使用体验| 人人妻人人爽人人添夜夜欢视频| 国产黄色视频一区二区在线观看| 青春草亚洲视频在线观看| 亚洲一码二码三码区别大吗| 日韩精品免费视频一区二区三区| av电影中文网址| 国产一区亚洲一区在线观看| 亚洲精品自拍成人| 99re6热这里在线精品视频| 久久久久久人妻| 99久久综合免费| 中国三级夫妇交换| 男女下面插进去视频免费观看| 一区二区日韩欧美中文字幕| 亚洲av综合色区一区| √禁漫天堂资源中文www| 国产精品成人在线| 看十八女毛片水多多多| 十八禁高潮呻吟视频| 999精品在线视频| 有码 亚洲区| 男的添女的下面高潮视频| 精品国产一区二区三区四区第35| 久久这里有精品视频免费| 国产免费又黄又爽又色| 国产成人午夜福利电影在线观看| 亚洲国产看品久久| 如何舔出高潮| av不卡在线播放| 国产 一区精品| 看十八女毛片水多多多| 伊人久久大香线蕉亚洲五| 日本猛色少妇xxxxx猛交久久| 黄频高清免费视频| 亚洲精品第二区| 色视频在线一区二区三区| 99热国产这里只有精品6| 国产白丝娇喘喷水9色精品| 日日啪夜夜爽| 国产精品久久久久成人av| 在线观看国产h片| 黄色一级大片看看| 亚洲美女搞黄在线观看| 人人妻人人添人人爽欧美一区卜| 日韩一本色道免费dvd| 欧美变态另类bdsm刘玥| 一区二区三区四区激情视频| 亚洲精品,欧美精品| 欧美最新免费一区二区三区| 久久久久久久国产电影| 成年人午夜在线观看视频| 人人妻人人添人人爽欧美一区卜| av福利片在线| 国产极品粉嫩免费观看在线| 2018国产大陆天天弄谢| 亚洲av电影在线观看一区二区三区| 毛片一级片免费看久久久久| 久久国内精品自在自线图片| 建设人人有责人人尽责人人享有的| 成年av动漫网址| 高清在线视频一区二区三区| 久久国产精品男人的天堂亚洲| h视频一区二区三区| 美女视频免费永久观看网站| 国产亚洲av片在线观看秒播厂| 男女无遮挡免费网站观看| 伊人久久国产一区二区| 老司机亚洲免费影院| 中文字幕色久视频| 成人毛片60女人毛片免费| 国产精品香港三级国产av潘金莲 | 精品人妻偷拍中文字幕| 青春草视频在线免费观看| 一级黄片播放器| 婷婷色综合www| 各种免费的搞黄视频| 美女高潮到喷水免费观看| 国产精品蜜桃在线观看| 91在线精品国自产拍蜜月| 亚洲欧美成人综合另类久久久| 亚洲中文av在线| 午夜福利一区二区在线看| 天天躁夜夜躁狠狠躁躁| 水蜜桃什么品种好| 久久国产亚洲av麻豆专区| 久久精品亚洲av国产电影网| videosex国产| 日韩一区二区视频免费看| 久久精品国产a三级三级三级| 国产精品国产三级国产专区5o| 免费在线观看视频国产中文字幕亚洲 | 色吧在线观看| 纵有疾风起免费观看全集完整版| 国产有黄有色有爽视频| 边亲边吃奶的免费视频| 精品人妻偷拍中文字幕| 国产麻豆69| 一级毛片电影观看| 欧美日韩视频高清一区二区三区二| 国产片内射在线| 中国国产av一级| av天堂久久9| 色哟哟·www| av网站在线播放免费| 亚洲伊人久久精品综合| 日韩中字成人| 最新的欧美精品一区二区| 永久网站在线| 久久免费观看电影| 日本vs欧美在线观看视频| 高清av免费在线| 性少妇av在线| 国产精品成人在线| 国产极品天堂在线| 精品国产露脸久久av麻豆| 国精品久久久久久国模美| 精品久久蜜臀av无| 国产女主播在线喷水免费视频网站| 亚洲第一av免费看| 久久久国产一区二区| 亚洲精品久久久久久婷婷小说| 美女大奶头黄色视频| 欧美在线黄色| 伊人久久大香线蕉亚洲五| 国产一级毛片在线| 黄网站色视频无遮挡免费观看| 天天躁日日躁夜夜躁夜夜| 日本爱情动作片www.在线观看| 高清av免费在线| 91aial.com中文字幕在线观看| 亚洲av国产av综合av卡| 久久久久国产网址| 亚洲人成网站在线观看播放| 久久狼人影院| 成人午夜精彩视频在线观看| 久久久久久久久久久免费av| 波多野结衣一区麻豆| 男女高潮啪啪啪动态图| 国产欧美日韩一区二区三区在线| 91精品国产国语对白视频| 久久久久国产一级毛片高清牌| av免费在线看不卡| 性少妇av在线| 精品国产一区二区久久| 国产欧美日韩综合在线一区二区| av免费观看日本| 看非洲黑人一级黄片| 日本午夜av视频| 成人手机av| 午夜日本视频在线| 欧美亚洲 丝袜 人妻 在线| 亚洲人成电影观看| 精品国产露脸久久av麻豆| 国产精品av久久久久免费| 你懂的网址亚洲精品在线观看| 在线看a的网站| 国产成人精品福利久久| 欧美日韩一区二区视频在线观看视频在线| 欧美黄色片欧美黄色片| 欧美黄色片欧美黄色片| 国产成人一区二区在线| 精品一品国产午夜福利视频| 久久久久久久亚洲中文字幕| 97精品久久久久久久久久精品| av在线播放精品| 久久久国产一区二区| xxx大片免费视频| 一区二区三区精品91| 久久精品国产亚洲av高清一级| 日韩av不卡免费在线播放| 免费人妻精品一区二区三区视频| 91aial.com中文字幕在线观看| 国产一区二区三区av在线| 两性夫妻黄色片| 汤姆久久久久久久影院中文字幕| 日产精品乱码卡一卡2卡三| 波多野结衣一区麻豆| 男人操女人黄网站| 极品人妻少妇av视频| 国产激情久久老熟女| 国产精品女同一区二区软件| 免费av中文字幕在线| 亚洲av福利一区| 一本色道久久久久久精品综合| 一级毛片 在线播放| 久久久精品国产亚洲av高清涩受| 日韩制服骚丝袜av| 免费人妻精品一区二区三区视频| 中文欧美无线码| 成人毛片a级毛片在线播放| 中文天堂在线官网| 久久99精品国语久久久| 丝袜人妻中文字幕| 国产精品成人在线| 欧美人与性动交α欧美精品济南到 | 国语对白做爰xxxⅹ性视频网站| 91在线精品国自产拍蜜月| 亚洲色图综合在线观看| 久久精品久久精品一区二区三区| 国产精品欧美亚洲77777| 在线观看美女被高潮喷水网站| 精品卡一卡二卡四卡免费| a级毛片黄视频| 最近手机中文字幕大全| 亚洲精品成人av观看孕妇| 美女视频免费永久观看网站| 精品少妇一区二区三区视频日本电影 | 女人高潮潮喷娇喘18禁视频| 下体分泌物呈黄色| 免费黄网站久久成人精品| 国产一区有黄有色的免费视频| 宅男免费午夜| 人人妻人人澡人人爽人人夜夜| 欧美激情 高清一区二区三区| 国产av一区二区精品久久| 日本欧美视频一区| 免费在线观看完整版高清| 国产不卡av网站在线观看| 久久国产精品男人的天堂亚洲| 91精品三级在线观看| 精品国产一区二区三区久久久樱花| 香蕉丝袜av| 亚洲av国产av综合av卡| 欧美日韩成人在线一区二区| 国产欧美日韩综合在线一区二区| av网站在线播放免费| 校园人妻丝袜中文字幕| 久久国产亚洲av麻豆专区| 婷婷色综合www| 成年人午夜在线观看视频| 18禁动态无遮挡网站| 国产精品香港三级国产av潘金莲 | 90打野战视频偷拍视频| 国产精品 欧美亚洲| 欧美少妇被猛烈插入视频| 免费看av在线观看网站| 亚洲情色 制服丝袜| 毛片一级片免费看久久久久| 国产黄色视频一区二区在线观看| 天天躁夜夜躁狠狠躁躁| 秋霞伦理黄片| 伦理电影大哥的女人| 午夜福利网站1000一区二区三区| av在线观看视频网站免费| 久久久久精品久久久久真实原创| 亚洲综合色网址| 日本爱情动作片www.在线观看| 亚洲av免费高清在线观看| 啦啦啦啦在线视频资源| 亚洲少妇的诱惑av| 久久精品aⅴ一区二区三区四区 | 亚洲国产日韩一区二区| 在线看a的网站| 五月伊人婷婷丁香| 夫妻午夜视频| 午夜老司机福利剧场| 少妇 在线观看| 老熟女久久久| 高清视频免费观看一区二区| 亚洲精品aⅴ在线观看| 国产精品偷伦视频观看了| 男人操女人黄网站| 精品无人区乱码1区二区| 久久久国产成人精品二区 | 亚洲专区国产一区二区| 国产精品亚洲一级av第二区| 99久久久亚洲精品蜜臀av| 欧美日本亚洲视频在线播放| 国产xxxxx性猛交| 亚洲激情在线av| 欧美日韩乱码在线| 欧美人与性动交α欧美精品济南到| 欧美成人免费av一区二区三区| 两个人看的免费小视频| 午夜福利影视在线免费观看| 热re99久久精品国产66热6| 亚洲成人免费av在线播放| 岛国视频午夜一区免费看| 交换朋友夫妻互换小说| 一级作爱视频免费观看| 精品久久久久久久久久免费视频 | 中文字幕高清在线视频| 亚洲欧美激情在线| 亚洲专区中文字幕在线| 一区二区日韩欧美中文字幕| 国产三级黄色录像| 久久人妻熟女aⅴ| 别揉我奶头~嗯~啊~动态视频| 国产精品永久免费网站| 国产熟女xx| 12—13女人毛片做爰片一| 日本 av在线| 亚洲狠狠婷婷综合久久图片| 国产黄色免费在线视频| 久久草成人影院| 午夜日韩欧美国产| 国产成人欧美| 两个人免费观看高清视频| 久久中文字幕人妻熟女| 欧美日韩视频精品一区| 日本三级黄在线观看| 国产成+人综合+亚洲专区| 日本免费一区二区三区高清不卡 | 日韩中文字幕欧美一区二区| 在线观看一区二区三区激情| 亚洲狠狠婷婷综合久久图片| 黄色片一级片一级黄色片| 人妻丰满熟妇av一区二区三区| 午夜激情av网站| 两个人免费观看高清视频| 国产伦人伦偷精品视频| 亚洲欧美激情综合另类| 我的亚洲天堂| av免费在线观看网站| 国产三级在线视频| 精品久久久久久久毛片微露脸| 中文字幕人妻熟女乱码| 男女高潮啪啪啪动态图| 久久 成人 亚洲| 精品少妇一区二区三区视频日本电影| 国产亚洲精品第一综合不卡| 国产精品九九99| 国产精品偷伦视频观看了| 美女扒开内裤让男人捅视频| 一本综合久久免费| 国产成人欧美| 国产免费av片在线观看野外av| 免费搜索国产男女视频| 国产亚洲欧美98| 9191精品国产免费久久| www.自偷自拍.com| 久久香蕉国产精品| 99在线视频只有这里精品首页| 国产高清videossex| 动漫黄色视频在线观看| 99热只有精品国产| 91国产中文字幕| 伦理电影免费视频| 亚洲国产精品合色在线| 亚洲成人精品中文字幕电影 | 啦啦啦 在线观看视频| 老鸭窝网址在线观看| 99香蕉大伊视频| 亚洲熟女毛片儿| 亚洲第一av免费看| 中文字幕另类日韩欧美亚洲嫩草| 男人操女人黄网站| 国产精品电影一区二区三区| 麻豆久久精品国产亚洲av | 国产极品粉嫩免费观看在线| 人妻丰满熟妇av一区二区三区| 国产99白浆流出| 岛国视频午夜一区免费看| 青草久久国产| 最近最新中文字幕大全电影3 | 99久久久亚洲精品蜜臀av| 一二三四社区在线视频社区8| 香蕉国产在线看| 人妻丰满熟妇av一区二区三区| 亚洲国产欧美日韩在线播放| 国产精品九九99| 很黄的视频免费| 美国免费a级毛片| 日韩欧美一区视频在线观看| 久久精品91无色码中文字幕| 国产aⅴ精品一区二区三区波| 国产一区二区三区视频了| 国产一区二区激情短视频| 国产极品粉嫩免费观看在线| 黄色毛片三级朝国网站| 男人舔女人的私密视频| 国产精品爽爽va在线观看网站 | 18禁黄网站禁片午夜丰满| 日韩大尺度精品在线看网址 | 国产精品乱码一区二三区的特点 | 亚洲成人免费av在线播放| 免费看十八禁软件| 亚洲午夜理论影院| 黄片小视频在线播放| 国产成人精品无人区| 欧美一级毛片孕妇| 日韩精品中文字幕看吧| 亚洲熟妇熟女久久| 精品久久久久久电影网| 国产精品久久久久成人av| 日本欧美视频一区| 久久久久久久久久久久大奶| xxx96com| 欧美亚洲日本最大视频资源| 女性生殖器流出的白浆| 国产区一区二久久| 搡老熟女国产l中国老女人| 日韩成人在线观看一区二区三区| 国产伦人伦偷精品视频| av天堂久久9| 亚洲少妇的诱惑av| 18禁美女被吸乳视频| 日本三级黄在线观看| 成人特级黄色片久久久久久久| 亚洲中文av在线| 国产精品自产拍在线观看55亚洲| 亚洲一区高清亚洲精品| 国产精品99久久99久久久不卡| 国产蜜桃级精品一区二区三区| 91国产中文字幕| 成人精品一区二区免费| 无遮挡黄片免费观看| 亚洲黑人精品在线| 国产精品一区二区免费欧美| 999久久久国产精品视频| av在线天堂中文字幕 | 韩国av一区二区三区四区| 亚洲av成人av| 丰满迷人的少妇在线观看| 黄色女人牲交| 日韩欧美在线二视频| 国产不卡一卡二| 自拍欧美九色日韩亚洲蝌蚪91| 欧美日本亚洲视频在线播放| 亚洲精华国产精华精| 麻豆一二三区av精品| 日韩精品免费视频一区二区三区| 久久九九热精品免费| 国产亚洲精品一区二区www| 欧美黑人精品巨大| 真人一进一出gif抽搐免费| 一边摸一边抽搐一进一出视频| 免费高清视频大片| 成人亚洲精品av一区二区 | 国产精品久久久久久人妻精品电影| 国产成人免费无遮挡视频| 一a级毛片在线观看| 青草久久国产| 高清欧美精品videossex| 美女高潮到喷水免费观看| 高清在线国产一区| 乱人伦中国视频| 最近最新免费中文字幕在线| 一级毛片高清免费大全| avwww免费| 日韩有码中文字幕| 曰老女人黄片| 欧美日本中文国产一区发布| 日本撒尿小便嘘嘘汇集6| 午夜激情av网站| 日韩三级视频一区二区三区| 欧美黄色淫秽网站| 一边摸一边做爽爽视频免费| 亚洲中文av在线| 欧美中文综合在线视频| 99久久久亚洲精品蜜臀av| 亚洲aⅴ乱码一区二区在线播放 | 日本wwww免费看| 老司机在亚洲福利影院| www.999成人在线观看| 欧美中文日本在线观看视频| 亚洲三区欧美一区| 极品教师在线免费播放| 久久午夜综合久久蜜桃| 久久久精品国产亚洲av高清涩受| 欧美人与性动交α欧美软件| 天堂动漫精品| 在线播放国产精品三级| 新久久久久国产一级毛片| 真人一进一出gif抽搐免费| 精品福利观看| 18禁美女被吸乳视频| 看免费av毛片| 美女午夜性视频免费| 国产欧美日韩综合在线一区二区| 久久久久国产精品人妻aⅴ院| 成人免费观看视频高清| 一区二区三区国产精品乱码| 国产精品九九99| 国产1区2区3区精品| 国产免费现黄频在线看| 午夜日韩欧美国产| 国产精品av久久久久免费| 国产精品野战在线观看 | 成人影院久久| 久热这里只有精品99| 日本三级黄在线观看| 亚洲视频免费观看视频| 国产高清国产精品国产三级| 男女做爰动态图高潮gif福利片 | 日韩人妻精品一区2区三区| 免费在线观看视频国产中文字幕亚洲| 19禁男女啪啪无遮挡网站| 久久久国产一区二区| 亚洲伊人色综图| 国产色视频综合| 九色亚洲精品在线播放| 亚洲av第一区精品v没综合| 久久久久久久久中文| 国产欧美日韩一区二区三| 国产单亲对白刺激| 每晚都被弄得嗷嗷叫到高潮| 丝袜人妻中文字幕| 99国产精品一区二区三区| 亚洲熟妇中文字幕五十中出 | ponron亚洲| 午夜福利在线观看吧| 又黄又粗又硬又大视频| 久久久水蜜桃国产精品网| 美女扒开内裤让男人捅视频| 韩国精品一区二区三区| 成人手机av| 97人妻天天添夜夜摸| 亚洲九九香蕉| 国产精品电影一区二区三区| 精品国产美女av久久久久小说| 日韩一卡2卡3卡4卡2021年| 亚洲中文字幕日韩| 国产成人精品无人区| 午夜91福利影院| 亚洲精品美女久久av网站| 多毛熟女@视频| 久久人人精品亚洲av| 三级毛片av免费| 国产高清视频在线播放一区| 免费看十八禁软件| 欧美不卡视频在线免费观看 | 日韩精品青青久久久久久| 精品熟女少妇八av免费久了| 国产99白浆流出| 青草久久国产| 日韩中文字幕欧美一区二区| av网站免费在线观看视频| 欧美精品啪啪一区二区三区| 国产成人精品在线电影| 亚洲av片天天在线观看| 国产成人精品在线电影| 一级毛片精品| 亚洲视频免费观看视频| 国产亚洲欧美精品永久| 在线观看一区二区三区激情| 可以免费在线观看a视频的电影网站| 黑人巨大精品欧美一区二区蜜桃| 真人一进一出gif抽搐免费| 亚洲人成网站在线播放欧美日韩| 麻豆av在线久日| 九色亚洲精品在线播放| 日韩人妻精品一区2区三区| 老司机亚洲免费影院| 少妇裸体淫交视频免费看高清 | 丝袜人妻中文字幕| 侵犯人妻中文字幕一二三四区| 99国产精品一区二区蜜桃av| 两性午夜刺激爽爽歪歪视频在线观看 | 黑人操中国人逼视频| 成人亚洲精品一区在线观看| 香蕉久久夜色| 亚洲国产精品一区二区三区在线| 色老头精品视频在线观看| 电影成人av| 国产无遮挡羞羞视频在线观看| 69精品国产乱码久久久| 欧美日本亚洲视频在线播放| 亚洲国产中文字幕在线视频| 88av欧美| 搡老熟女国产l中国老女人| 亚洲国产欧美一区二区综合| 麻豆久久精品国产亚洲av | 伊人久久大香线蕉亚洲五| 女人被狂操c到高潮| 日韩欧美一区二区三区在线观看| 国产aⅴ精品一区二区三区波| 一进一出好大好爽视频| 久久国产精品人妻蜜桃| 欧美激情 高清一区二区三区| 搡老乐熟女国产| 在线免费观看的www视频| 久久人妻av系列| 十八禁网站免费在线| 麻豆一二三区av精品| 99国产综合亚洲精品| 久久久久国产精品人妻aⅴ院|