Binary diffing is a process to discover the differences and similarities in functionality between two binary programs. Previous research on binary diffing approaches it as afunction matchingproblem to formulate an initial 1:1 mapping between functions, and later a sequence matching ratio is computed to classify two functions being anexactmatch, apartialmatch orno-match. The accuracy of existing techniques is best only when detecting exact matches and they are not efficient in detecting partially changed functions; especially those with minor patches. These drawbacks are due to two major challenges (i) In the 1:1 mapping phase, using a strict policy to match function features (ii) In the classification phase, considering an assembly snippet as a normal text, and using sequence matching for similarity comparison. Instruction has a unique structure i.e. mnemonics and registers have a specific position in instruction and also have a semantic relationship, which makes assembly code different from general text. Sequence matching performs best for general text but it fails to detect structural and semantic changes at an instruction level thus, its use for classification produces many false results. In this research, we have addressed the aforementioned underlying challenges by proposing a two-fold solution. For the 1:1 mapping phase, we have proposed computationally inexpensive features, which are compared with distance-based selection criteria to map similar functions and filter unmatched functions. For the classification phase, we have proposed a Siamese binary-classification neural network where each branch is an attention-based distributed learning embedding neural network — that learn the semantic similarity among assembly instructions, learn to highlight the changes at an instruction level and a final stage fully connected layer learn to accurately classify two 1:1 mapped function either anexactor a partial match. We have used x86 kernel binaries for training and achieved$\sim 99\%$classification accuracy; which is higher than existing binarydiffingtechniques and tools.

This paper introduces the concept of a new service for social Internet of Vehicles (IoV)-based clouds called incentives-based vehicle witnesses as a service (IVWaaS), which employs vehicles moving on the road as the witnesses to designated events. Specifically, we focus on two key enablers, a new secure and privacy preserving service framework as well as a new incentive mechanism to promote the wide adoption of the aforementioned social service. In IVWaaS, when confronted any events, the vehicles in the vicinity with mounted cameras collaborate with other roadside cameras to take pictures of the site of interest around them, and send the pictures to the cloud infrastructure anonymously so that the privacy of the vehicles can be preserved. To stimulate active participation from the users, we also introduce a new privacy-aware incentives mechanism called privacy-aware proportionate receipt collection, in which the contributors are credited according to their contribution to the service and can claim their incentives in a privacy-aware fashion. Service providers can also use the stored pictures as “on-demand picture service.” Other law enforcement agencies can obtain the stored pictorial information and use it as forensics in the investigations.

Summary The mobile cloud is a highly heterogenous and constantly evolving network of numerous portable devices utilizing the powerful back‐end cloud infrastructure to overcome their severe deficiency in computing resource and offer various services such as data sharing. Inherently, in mobile cloud, the risk of user privacy invasion by the cloud operator is high. The conditional proxy re‐encryption (CPRE) is a useful concept for secure group data sharing via cloud while preserving the privacy of the shared data from any unintended third parties including the cloud operator. Unfortunately, the state‐of‐art CPRE is not particularly designed for mobile cloud environment and therefore imposes heavy burdens to the weak mobile cloud clients. This paper introduces a new CPRE scheme, namely the CPRE for mobile cloud, which utilizes the back‐end cloud to the extreme extent so that the overhead of terminals is drastically reduced. Specifically, our scheme outsources a significant amount of computation overhead caused by the following functions at terminals: (a) re‐encryption key generation, (b) condition value change, and (c) decryption, to the cloud. The proposed scheme also allows users to verify the correctness of outsourced computation under refereed delegation of computation model. Our simulation results show CPRE for mobile cloud that outperforms its existing alternatives. Copyright © 2016 John Wiley & Sons, Ltd.

Recently an online electric vehicle (OLEV) concept has been introduced, where vehicles are propelled by the wirelessly transmitted electrical power from the infrastructure installed under the road while moving. The absence of secure-and-fair billing is one of the main hurdles to widely adopt this promising technology. This paper introduces a new secure and privacy-aware fair billing framework for OLEV on the move through the charging plates installed under the road. We first propose two extreme lightweight mutual authentication mechanisms, a direct authentication and a hash chain-based authentication between vehicles and the charging plates that can be used for different vehicular speeds on the road. Second, we propose a secure and privacy-aware wireless power transfer on move for the vehicles with bidirectional auditability guarantee by leveraging game theoretic approach. Each charging plate transfers a fixed amount of energy to the vehicle and bills the vehicle in a privacy-aware way accordingly. Our protocol guarantees secure, privacy-aware, and fair billing mechanism for the OLEVs while receiving electric power from the infrastructure installed under the road. Moreover, our proposed framework can play a vital role in eliminating the security and privacy challenges in the deployment of power transfer technology to the OLEVs.

Mobile social networks (MSNs) are getting increasingly popular day by day. With the help of MSNs people can connect with each other online as well as offline. One of the famous applications of MSNs is profile matchmaking in which users share their interests in order to find mutual friends. However, the user of such application can be a victim of various privacy related attacks due to the revelations of user's personal interests during profile matchmaking. More recent dimension of MSN is encounter-based social networks. In this scenario, the users make social contacts while sharing a common location and time. Mobile devices record the encounter information when two users are within physical proximity and at a later time, their encounter history should be matched when they try to find each other again. In this paper, we propose a privacy preserving matchmaking protocol in which users share the encounter information and later utilize a cloud server to post encounter information in order to privately match their profiles with unknowns to whom they shared the encounter. In our protocol, neither the users nor the cloud server is able to discover the interests of either participants during matchmaking. To the best of our knowledge, this approach is the first attempt that combines encounter based social networks and fine-grained profile matchmaking. Towards the end of the paper, we present security analysis as well as performance evaluation that shows the effectiveness and feasibility of our protocol.

Vehicular ad hoc network (VANET) is an application of intelligent transportation system (ITS) with emphasis on improving traffic safety as well as efficiency. VANET can be thought as a subset of mobile ad hoc network (MANET) where vehicles form a network by communicating with each other (V2V) or with infrastructure (V2I). Vehicles broadcast not only traffic messages but also safety critical messages such as electronic emergency braking light (EEBL). A misuse of this application may result in a traffic accident and loss of life at worse. This situation makes vehicles' authentication a necessary requirement in VANET. During authentication, vehicle's privacy related data such as vehicle and owner's identity and location information should be kept private in order to prevent an attacker from stealing this information. This paper presents a cloud-assisted conditional privacy preserving authentication (CACPPA) protocol for VANET. CACPPA is a hybrid approach that utilizes both the concept of pseudonym-based approaches and group-signaturebased approaches but cleverly avoids the inherent drawbacks of these approaches. CACPPA neither requires a vehicle to manage a certificate revocation list nor does it require vehicle to manage any groups. In fact an efficient cloud-based certification authority is used to assist vehicles getting credentials and subsequently using them during authentication. CACPPA provides conditional anonymity that a vehicle's anonymity preserved only until it honestly follows the protocol. Furthermore, we analyze CACPPA with various attack scenarios, present a computational and communication cost analysis as well as comparison with existing approaches to show its feasibility and robustness.

Location based services (LBSs), which are useful applications of mobile online social network (mOSN), exploit various geographic properties. Location sharing helps people to share their current locations with designated friends and is one important primitive to construct the LBSs. The recent reports showed that a poorly designed location sharing scheme could easily allow the privacy of users to be violated. Over years, lots of efforts are made to provide a privacy-preserving location sharing, but none of them is satisfactory. To address this issue, we introduce a new location sharing scheme in mOSNs with a strong user privacy protection mechanism such that (a) the user's current location as well as (b) the list of friends who will learn the user's current location will be protected from any unintended entity, while the designated friends in the list will learn the exact location of the user. For this purpose, we introduce a new cryptography primitive called the functional pseudonym scheme based on Lagrange polynomial with the public social network IDs of the designated friends. Then, the pseudonym of a user is posted on the server along with the current location of the user. While each user can see every posted messages (pseudonym and location pairs), the actual identify of the originator of each pair can be verified only by designated friends, whose identities are used to compute the pseudonym. Most importantly, unlike any of the existing counterparts, our scheme does not assume neither a trusted server nor pre-established secret among the friends.

Abstract The Notice of Proposed Rulemaking 13‐22 released by Federal Communications Commission unlocks the Dedicated Short Range Communication (DSRC) spectrum for Wi‐Fi availability, which undoubtedly brings unpredictable effects to the new‐emerging vehicular applications and services. To efficiently harmonize the spectrum operation between DSRC and Wi‐Fi networks, several dynamic spectrum‐sharing schemes are already proposed to improve the spectral efficiency over a limited bandwidth situation and as well to satisfy the ever‐increasing demand for bandwidth resource. Different from most previous literature that mainly focused on the performance analysis of cellular‐network‐centric spectrum sharing, we aim to analyze the performance of the mainstream dynamic spectrum‐sharing schemes specially designed for the coexistence of DSRC and Wi‐Fi networks against various combinations of network parameters through a hybrid network model and performance indicators. We employ the Poisson point process to model a hybrid network where DSRC vehicles and Wi‐Fi devices coexist, and introduce the performance indicators of spectrum efficiency and data rate to assess the utility of different spectrum sharing candidates. Through the presented hybrid model and performance indicators, we collect extensive numerical and simulation results to investigate four typical spectrum allocation schemes for DSRC and Wi‐Fi coexistence, that is non‐sharing scheme, original sharing scheme, and Qualcomm's and Cisco's proposals, respectively. The results show that the dynamic spectrum sharing in the 5.9‐GHz band can significantly raise the performance of Wi‐Fi network without excessively degrading the DSRC system, and especially the Cisco's proposal prefers to protect the DSRC profit while the Qualcomm's draft favors Wi‐Fi exclusively. Copyright © 2016 John Wiley & Sons, Ltd.

Recently a new concept of online electric vehicle (OLEV) has been introduced in South Korea, where vehicles are propelled through the transmitted energy from the infrastructure installed underneath the road. However, for billing and audit reasons only authentic vehicles with necessary credentials are allowed to charge their batteries and pay the designated amount to the service provider. Moreover, due to the massive budget requirements for such infrastructure, only designated road segments will offer the charging service. As a result, a tradeoff solution to the charging of electric vehicles is needed to both fulfill the charging requirements of the electric vehicles and reduce the upfront costs for the service providers. To obtain electric charge from the charging plates beneath the road, vehicles need to authenticate themselves beforehand for twofold purposes: to bill the vehicles accordingly and to let the revocation authorities revoke the vehicle in case of a dispute. In this paper, we use the core concept of the OLEV and introduce extreme lightweight privacy-aware authentication schemes for charging-on-the-move through the charging plates installed under the road. More precisely we propose two mutual authentication mechanisms between charging plates and the vehicles, a direct authentication and a hash chain-based authentication. In the direct authentication scheme, we leverage multiple pseudonyms for conditional privacy. Vehicles use different pseudonyms every time they use the charging-on-the-move service. Whereas in case of hash chain-based authentication mechanism, the vehicles mutually authenticate with charging plates through service provider. Our proposed authentication mechanisms preserve conditional privacy throughout the protocol and is computationally lightweight than the existing mechanisms.

Vehicular ad hoc network (VANET) is gaining significant popularity due to their role in improving traffic efficiency and safety. However, communication in VANET needs to be secure as well as authenticated. The vehicles in the VANET not only broadcast traffic messages known as beacons but also broadcast safety critical messages such as electronic emergency brake light (EEBL). Due to the openness of the network, a malicious vehicles can join the network and broadcast bogus messages that could result in accident. On one hand, a vehicle needs to be authenticated while on the other hand, its private data such as location and identity information must be prevented from misuse. In this paper, we propose an efficient pseudonymous authentication protocol with conditional privacy preservation to enhance the security of VANET. Most of the current protocols either utilize pseudonym based approaches with certificate revocation list (CRL) that causes significant communicational and storage overhead or group signature based approaches that are computationally expensive. Another inherent disadvantage is to have full trust on certification authorities, as these entities have complete user profiles. We present a new protocol that only requires honest-but-curious behavior from certification authority. We utilize a mechanism for providing a user with two levels of pseudonyms named as base pseudonym and short time pseudonyms to achieve conditional privacy. However, in case of revocation, there is no need to maintain the revocation list of pseudonyms. The inherent mechanism assures the receiver of the message about the authenticity of the pseudonym. In the end of the paper, we analyze our protocol by giving the communication cost as well as various attack scenarios to show that our approach is efficient and robust.

The term "smart grid" refers to the next generation power supply system. A smart meter, an essential component of the grid system, is installed at each housing unit and acts as an agent for the unit. While the smart meter is a key enabler of great opportunities and conveniences in smart grid, it is susceptible to various cyber-security attacks, especially privacy invasion from electricity providers. Trusted third party (TTP) and homomorphic encryption are two favorite tools to deal with this issue in the literature. Unfortunately, the use of TTP does not completely eliminate the privacy risk. On the other hand, the use of homomorphic encryption makes it harder for the providers to support various services whose demand can be highly diversified. In this paper, we introduce a drastically new approach to deal with the consumer privacy issue in smart grid. Our key idea is let each consumer to determine the frequency of the measurement report. In this way, each consumer can responsibly make a trade-off between the level of privacy preservation with the quality of the services it will receive.

In the recent past, a new concept termed as VANET-based clouds evolved from traditional VANET incorporating both VANET and cloud computing technologies in order to provide vehicle drivers, passengers, and consumers with safe, reliable, and infotainment-rich services while driving on the roads. In this paper, we use a framework of VANET-based clouds proposed by Hussain et al. namely VuC (VANET using Clouds) and define another layer TIaaS (Traffic Information as a Service) atop the cloud computing stack. TIaaS layer provides vehicular nodes (more precisely subscribers) with fine-grained traffic information in a secure way. Additionally our proposed scheme provides security, privacy, and conditional anonymity which are of prime concern in VANET clouds.

Technological advancements in mobile technology and cloud computing open the door for another paradigm known as Mobile Cloud Computing (MCC). This integration of cloud computing and mobile technology gives numerous facilities to a mobile user, such as the ubiquitous availability of Location Based Services (LBS). The utilization of these LBS services require the knowledge of a user's location, hence threatens the privacy of a user. In this paper we take advantages of ultra-fast processing and reliability of cloud computing and aim to solve the privacy threat issue faced by a mobile user while getting LBS services. We propose a model that utilizes a cloud based server which helps in making of a cloaking region. We highlight that how our model utilizes an untrusted cloud based server and eliminates the use of a trusted anonymizer while providing LBS services to a user securely and anonymously.

Inspired by the dramatic evolution of VANE clouds, this paper proposes a new VANET-cloud service called VWaaS (Vehicle Witnesses as a Service) in which vehicles moving on the road serve as anonymous witnesses of designated events such as a terrorist attack or a deadly accident. When confronted the events, a group of vehicles with mounted cameras collaborate with roadside stationary cameras to take pictures of the site of interest (SoI) around them, and send the pictures to the cloud infrastructure anonymously. The pictures are sent to the cloud in a way that the privacy of the senders can be protected, and kept by the cloud for future investigation. However, for the case that the pictures are used as an evidence of court trial, we made the privacy protection to be conditional and thus can be revoked by authorized entity(s) if necessary.

The foreseen dream of reliable, safe, and comfortable driving experience is yet to become reality since automobile industries are testing their waters for VANET (Vehicular Ad Hoc NETwork) deployment. But nevertheless, security and privacy issues have been the root cause of hindrance in VANET deployment. Recently, VANET evolved to VANET-based clouds as a result of resources-rich high-end cars. Soon after, Hussain et al. defined different architectural frameworks for VANET-based clouds. In this paper, we aim at a specific framework namely VuC (VANET using Clouds) where VANET and CC (Cloud Computing) cooperate with each other in order to provide VANET users (more precisely subscribers) with services. We propose a lightweight privacy-aware revocation and route tracing mechanism for VuC. Beacons broadcasted by vehicles are stored in cloud infrastructure as cooperation from VANET and after processing, cloud provides VANET subscribers with services. Revocation authorities can revoke and trace the path taken by the target node for a specified timespan by exploiting the beacons stored in the cloud. Our proposed scheme is secure, preserves conditional privacy, and is computationally less expensive than the previously proposed schemes.

Despite the surge in Vehicular Ad Hoc NETwork (VANET) research, future high-end vehicles are expected to under-utilize the on-board computation, communication, and storage resources. Olariu et al. envisioned the next paradigm shift from conventional VANET to Vehicular Cloud Computing (VCC) by merging VANET with cloud computing. But to date, in the literature, there is no solid architecture for cloud computing from VANET standpoint. In this paper, we put forth the taxonomy of VANET based cloud computing. It is, to the best of our knowledge, the first effort to define VANET Cloud architecture. Additionally we divide VANET clouds into three architectural frameworks named Vehicular Clouds (VC), Vehicles using Clouds (VuC), and Hybrid Vehicular Clouds (HVC). We also outline the unique security and privacy issues and research challenges in VANET clouds.

Route tracing allows authorities to determine the route traversed by a vehicle, which can be used valuably in crime investigations. In VANETs (Vehicular Ad hoc NETwork), a conditional anonymity scheme is normally used to meet the conflicting demands of the government (authentication) and users (privacy). These schemes provide a mechanism to revoke the anonymity of individual beacons sent by vehicles. However, using this mechanism to trace a route of a vehicle violates privacy of many other vehicles. In this paper, we provide a simple route tracing mechanism that reveals only the route of the targeted vehicle. The proposed method can be easily added to any existing VANET solutions and also includes a way to prevent authorities from abusing this new function.

An iterative learning algorithm called PRLAB is described for the discrete bidirectional associative memory (BAM). Guaranteed recall of all training pairs is ensured by PRLAB. The proposed algorithm is significant in many ways. Unlike many existing iterative learning algorithms, PRLAB is not based on the gradient descent technique. It is a novel adaptation from the well-known relaxation method for solving a system of linear inequalities. The algorithm is very fast. Learning 200 random patterns in a 200-200 BAM takes only 20 epochs on the average. PRLAB is highly insensitive to learning parameters and the initial configuration of a BAM. It also offers high scalability for large applications by providing the same high performance when the number of training patterns are increased in proportion to the size of the BAM. An extensive performance analysis of the new learning algorithm is included.