Recent studies have proved the importance of intact lateral trochanteric wall, thus buttressing/fixing the broken lateral trochanteric wall irrespective of the implant, is likely to improve the alignment and outcome. We compared the outcome of lateral wall buttressing by trochanteric buttress plate (TBP) supplemented to proximal femoral nailing versus proximal femoral nailing alone in patients of broken lateral wall intertrochanteric fractures. Sixty patients of intertrochanteric factures (IT) of femur with broken lateral wall were randomized into group A or B and were treated with either proximal femoral nail (PFN) alone or proximal femoral nail augmented with trochanteric buttress plate (PFN + TBP), respectively. The TBP plate used was primarily fixed to proximal femur via 8 mm hip screw and 6.4 mm antirotation screw of the PFN. Operative time, blood loss, radiation exposure, quality of reduction, functional outcome, union time, and complications were compared. The mean age was 60.03 + 7.60 (range 42 to 70 years), with male to female ratio of 4:1 and left to right ratio of approximately 1:1. The mean follow up in the series was 16.2 months (range 13 to 36 months). Mean duration of surgery, mean intra-operative blood loss, and mean number of exposures in group A (PFN alone) were 64.88 + 12.24 min (48 to 88), 93 + 1.18 ml (60 to 120), and 32.13 (24 to 46) and in group B (PFN with TBP plate) were 91.86 + 12.78 min (70 to 122 min), 144.8 + 3.6 ml (116 to 208 ml), and 56.6 (38 to 112), respectively. Twenty-five patients and 28 patients in groups A and B respectively achieved score of 4 Chang quality reduction. Mean union time was 13.4 weeks in group A whereas in group B was 11.6 weeks. Mean HHS score in group A was 87.86 with 90% patients in comparison to 94.13 and 97% cases having excellent to good results in group B. In group A, 24 patients, while 29 patients in group B, had excellent to good results. Four patients had hip pain, four had impingement of screws, two had screw migration, three had Z/reverse effect, and four patients had shortening of more than 1 cm in group A. In group B, only one patient had impingement and none of the patient had hip pain, infection, implant failure, Z effect, or shortening. The lateral trochanteric wall in IT fractures is significantly important, and when the lateral wall is broken, it can lead to poor results. TBP plate which is applied laterally on femur along with nail and fixing the plate with hip screw and antirotational screw provides faster union, early weight bearing, better reduction, and so better hip functions. TBP can be used successfully to augment, fix, or buttress the lateral trochanteric wall giving excellent to good results but at the cost of surgical time, blood loss, and radiation exposure.

In recent times, the wireless sensor network (WSN) has become an integral part of daily life. WSN forms the necessary foundation for several important applications such as animal monitoring, border surveillance, asset monitoring, etc. These applications help maintain the confidentiality of the location of the occurring event from the attacker. The properties of the sensor nodes such as limited energy source, communication capability, memory, and network deployment at a large scale make it challenging to maintain the location privacy of a source node. To secure the source node location, this paper presents a source location privacy protection scheme that is based on random rings and a limited hop fake packet routing scheme (SLP-RRFPR). In the proposed scheme, an event packet is forwarded away from the base station by the random routing with confounding transmission to change the attacker’s backtracking process. Afterward, it follows the random routing, where the phantom node forwards the fake packet to other randomly selected nodes. In the last phase, a real packet is transmitted at the base station by ring routing. The simulation results of the proposed SLP-RRFPR are compared with phantom, baseline, probabilistic, source location privacy protection scheme based on ring-loop routing (SLPRR), and source location protection protocol based on dynamic routing (SLPDR). The simulation results show that the proposed SLP-RRFPR performs better than the compared protocol for various performance metrics, such as safety time, transmission delay, network lifetime, and randomness in the packet path.

Computation of complex mathematical problems are always a challenge of resource constrained clients. A client can outsource the computations to resource abundant cloud server for execution. But this arrangement brings many security and privacy challenges. In this paper, we have presented a secure and efficient algorithm for general computation and scientific problem i.e. matrix multiplication. The proposed algorithm is inspired by the existing algorithm, but we believe that it is imperative to improve the algorithm to enable secure outsourcing of computation. The previous state-of-the art algorithm for matrix multiplication is vulnerable to the Cipher-Text Only Attack (COA) along with Chosen Cipher-Text Attack (CCA) and Known Plain-Text Attack (KPA) and reveal information about the client's data. Hence fails the security requirements of the outsourcing algorithm. The proposed work retains the efficiency benefit of state-of-the-art algorithm, additionally defended the client data against (COA) along with (CCA) and Known Plain-Text Attack (KPA).

Cloud computing offers an economical, convenient and elastic pool of computing resources over the internet. It enables computationally weak client to execute large computations by outsourcing their computation load to the cloud servers. However, outsourcing of data and computation to the third-party cloud servers bring multifarious security and privacy challenges that needed to be understood and address before the development of outsourcing algorithm. In this paper, the authors propose solutions for matrix-chain multiplication (MCM) problem. Our goal is to minimize the execution burden on the client without sacrificing the confidentiality and integrity of the input/output. Conventionally, the complexity of matrix-chain multiplication is O ( n 3 ). After leveraging the facility of outsourcing, the client-side complexity reduces to O ( n 2 ). In the proposed algorithm, the client employs some efficient linear transformation schemes, which preserve the data confidentiality. It also developed a novel result verification scheme, which verifies the result with modest burden and high probability and maintain the integrity of computed result. The analytical analysis of algorithm depicted that the algorithm is simultaneously meeting the design goals of correctness, security, efficiency and verifiability. We conduct many experiments to validate the algorithm and demonstrate its practical usability. The algorithm is implemented on public cloud “ Amazon EC2 ”, and found that the proposed outsource algorithm performs 11.655 times faster computation of matrix-chain multiplication than the direct implementation.

The growth of the cloud computing services and its proliferation in business and academia has triggered enormous opportunities for computation in third-party data management settings. This computing model allows the client to outsource their large computations to cloud data centers, where the cloud server conducts the computation on their behalf. But data privacy and computational integrity are the biggest concern for the client. In this article, the authors attempt to present an algorithm for secure outsourcing of a covariance matrix, which is the basic building block for many automatic classification systems. The algorithm first performs some efficient transformation to protect the privacy and verify the computed result produced by the cloud server. Further, an analytical and experimental analysis shows that the algorithm is simultaneously meeting the design goals of privacy, verifiability and efficiency. Also, found that the proposed algorithm is about 7.8276 times more efficient than the direct implementation.

Cloud computing is poised to equip every computing node over the internet with the sheer computing power of data centers. It provides unlimited computing resources economically, conveniently, and ubiquitously in pay-per-use manner. It enables computationally weak client to execute large computations by outsourcing their computation load to the cloud servers. However, outsourcing of data and computation to the third-party cloud servers brings multifarious security and privacy challenges that needed to be understood and address before the development of outsourcing algorithm. The goal of this article is to show that the monomial matrix-based constructions are unable to protect the input and output privacy. Therefore, it becomes imperative to propose an improved algorithm to remove the shortcomings of the existing algorithm while retaining all the merits of previous algorithm.

Cloud computing has become a revolution in the field of computing, which enables flexible, on-demand, usage of computing resources in pay-as-per-use model. However, data and computation go to some third-party cloud server beyond the physical control of client escalates various privacy and security concern. This paper proposes an improved outsourcing algorithm for system of linear equation (SLE). The improvements are first, the existing work uses expensive cryptographic computation such as Paillier encryption for security arrangement, the proposed solution does not use such cryptographic primitives rather uses efficient linear transformation method. Secondly, the previous work uses an iterative process, which required 𝐿 rounds of communication between the client and cloud server, at the same time each iteration causes burden of a decryption followed by a matrix vector multiplication. However, the proposed solution required an optimal one round of communication and one-time transformation and retransformation service. Third, the previous work gives result verification method with (1/2l) error probability, the value of 𝑙 is a trade-off between security and efficiency. However, the proposed solution gives an error checking with an optimal probability of one. Moreover, a security analysis has been performed on the previous method, which proves marginal security arrangement due to inappropriate use of the Paillier encryption scheme. The proposal has been verified through theoretical and experimental analysis, which demonstrate the superiority of the proposed algorithm as compared to the existing algorithm.

Cloud computing offers an economical solution to the computationally weak clients. It enables the client to execute large computations beyond their computation capacity by outsourcing their computation load to the massive cloud servers. However, outsourcing of data and the computation to a third-party cloud server bring many security and privacy concern. In this paper, we are addressing the problem of system of linear equation (LE), which is a basic engineering and scientific problem that has application in various areas. We are employing efficient linear transformation technique using the concepts of linear algebra. The proposed algorithm transform the dataset to some other form to hide the original dataset. The algorithm provides a non-interactive proof of solution therefore, the algorithm executes with an optimal one round of communication. The proposed algorithm also employs an efficient result verification method to perceive deceitful server behavior with an optimal probability, thus preserving the data integrity. The result verification method is very efficient and run with modest overhead. The proposal has been verified through theoretical and experimental analysis to demonstrate the practical usability of the proposed algorithm.

Cloud computing has become ubiquitous, offers an economical solution for convenient on-demand access to computing resources, which enable the resource-constrained clients to execute extensive computation. However, outsourcing of data and computation to the cloud server is a great cause of concern, such as confidentiality of input/output and verifiability of the result. This paper addresses the problem of designing outsourcing algorithm for linear regression analysis (LR), which is an important data analysis technique and widely applied across multiple domains. The outsourcing framework illustrated by the following scenario: a client is having a large dataset and needs to perform regression analysis, but unable to process due to lack of computing resources. Therefore, the client outsources the computation to the cloud server. In the proposed LR outsourcing algorithm, the client outsources LR problem to the cloud server without revealing to them either the input dataset and the output. The algorithm is a non-interactive solution to the client, it sends only input and receives output along with the proof of verification from the cloud server. The client in the proposed algorithm able to verify the correctness of result with an optimal probability. The analytical analysis shows that the algorithm is successfully meeting the challenges of correctness, security, verifiability, and efficiency. The experimental evaluation validates the proposed algorithm. The result analysis shows that the algorithm is highly efficient and endorses the practical usability of the algorithm.

The rapid development of cloud computing services and expansion of mobile computing devices have made computation outsourcing a promising solution for execution of extensive computation. In this framework, a computationally weak client outsources its large computation load to a cloud server. However, outsourcing of data and computation to a cloud server brings many security and privacy concern. In this paper, we are addressing matrix multiplication (MM) problem, because MM is a computation-intensive problem and useful in many domains. In the proposed MM outsourcing algorithm, the client outsources input dataset to the cloud server without revealing to them both the input dataset and the output. The algorithm is a non-interactive solution to the client, it sends only input and receives output along with the proof of verification from the cloud server. Further, this work extends the definition of verifiable computation to public verifiable computation, which allows participating worker (not only the client) to verify the correctness of the result computed on the cloud server. The analytical analysis shows that the algorithm is successfully meeting the challenges of correctness, security, verifiability, and efficiency. The practical evaluation validates the proposed algorithm. The result analysis shows that the algorithm is highly efficient and endorses the practical usability of the algorithm.

IT industry has been experiencing the benefits of outsourcing for years, since the outsourcing brings down both the capital and operational expenditure. Similarly, cloud computing provides storage, computation, and other specialized services on demand to customers over the internet at a very generous cost. However, outsourcing data and computation to a third party cloud server is a great cause of concern to the client because a customer physically loses control over their sensitive/classified data and computation. The loss of physical control over the data and computation is the main issue for a client that makes him feel insecure using cloud computing services. The solution to address outsourcing issues, first, the cloud service provider must be honest by providing correct and secure computation; second, the outsourced data and computation shall be verifiable to customers in terms of confidentiality and integrity. In this paper, we investigate the problem of regression analysis, outsourcing problem and devise a secure and efficient outsourcing algorithm which provides security for the input and also provide safeguards to the output result computed on cloud servers. Further, a novel and efficient result verification technique have been developed to detect server misbehavior and cheating with optimal probability of 1. Furthermore, theoretical and experimental analysis has compared with existing algorithm to demonstrate the efficiency, security and effectiveness of the proposed algorithms.

Cloud computing is becoming an increasingly admired paradigm that delivers high-performance computing resources over the Internet to solve the large-scale scientific problems, but still it has various challenges that need to be addressed to execute scientific workflows. The existing research mainly focused on minimizing finishing time (makespan) or minimization of cost while meeting the quality of service requirements. However, most of them do not consider essential characteristic of cloud and major issues, such as virtual machines (VMs) performance variation and acquisition delay. In this paper, we propose a meta-heuristic cost effective genetic algorithm that minimizes the execution cost of the workflow while meeting the deadline in cloud computing environment. We develop novel schemes for encoding, population initialization, crossover, and mutations operators of genetic algorithm. Our proposal considers all the essential characteristics of the cloud as well as VM performance variation and acquisition delay. Performance evaluation on some well-known scientific workflows, such as Montage, LIGO, CyberShake, and Epigenomics of different size exhibits that our proposed algorithm performs better than the current state-of-the-art algorithms.

Today, every computing node is generating large amounts of data that cannot be stored on these resource constrained devices, so cloud computing provides an ample opportunity to use their storage facility. However, outsourcing personal data to third party storage facility inversely brings security and privacy concerns that make user to reluctance to use cloud computing facility. In this research paper we have made an attempt to provide end to end security of client data. The paper has designed a cloud storage framework that provides secure data outsourcing and retrieval of client data to ensure the integrity of data. Data outsourcing has threat to privacy at two point data in-transit and at rest. To provide assurance of data privacy encrypts the data before outsource using SSL. Further, the Predicate Based Encryption scheme has used to encrypt the client data at rest. The method focuses on multiple data owner scenario, and divides the users' data depending upon user, severity rating (SR) such as low, and medium and high severity level. The experimental analysis has proven the combined approach provide a high degree of privacy preference of client data.

This paper describes a geometric algorithm for automated design of multi-stage molds for manufacturing multi-material objects. In multi-stage molding process, the desired multi-material object is produced by carrying out multiple molding operations in a sequence, adding one material in the target object in each mold-stage. We model multi-material objects as an assembly of single-material components. Each mold-stage can only add one type of material. Therefore, we need a sequence of mold-stages such that (1) each mold-stage only adds one single-material component either fully or partially, and (2) the molding sequence completely produces the desired object. In other to find a feasible mold-stage sequence, our algorithm decomposes the multi-material object into a number of homogeneous components to find a feasible sequence of homogeneous components that can be added in sequence to produce the desired multi-material object. Our algorithm starts with the final object assembly and considers removing components either completely or partially from the object one-at-a-time such that it results in the previous state of the object assembly. If the component can be removed from the target object leaving the previous state of the object assembly a connected solid then we consider such decomposition a valid step in the stage sequence. This step is recursively repeated on new states of the object assembly, until the object assembly reaches a state where it only consists of one component. When an object-decomposition has been found that leads to a feasible stage sequence, the gross mold for each stage is computed and decomposed into two or more pieces to facilitate the molding operation. We expect that our algorithm will provide a step towards automating the design of multi-stage molds and therefore will help in reducing the mold design lead-time for multi-stage molds.

This paper describes a feature-based approach for design of multi-piece sacrificial molds. Our mold design algorithm consists of the following three steps. First, the desired gross mold shape is formed based on the feature-based description of the part geometry. Second, if the desired gross mold shape is not machinable as a single piece, the gross mold shape is decomposed into simpler geometric components to make sure that each component is manufacturable using 3-axis CNC machining. The decomposition is performed to ensure that each component is accessible to end-milling tools, and decomposed components can be assembled together to form the gross mold shape. Finally, assembly features are added to mold components to facilitate assembly of mold components and eliminate unnecessary degree of freedoms from the final mold assembly. We expect that our decomposition algorithm will provide a step towards the necessary foundations for automating the design of multi-piece mold and therefore will help in significantly reducing the mold design and manufacturing lead-time.

This paper describes a feature-based algorithm for automated design of multi-piece sacrificial molds. Our mold design algorithm consists of the following three steps. First, the desired gross mold shape is created based on the feature-based description of the part geometry. Second, if the desired gross mold shape is not machinable as a single component, then the gross mold shape is decomposed into simpler geometric components to make sure that each component is machinable using 3-axis CNC machining. The decomposition is performed to ensure that each component is accessible to end-milling tools, and decomposed components can be assembled together to form the gross mold shape. Finally, assembly features are added to mold components to eliminate unnecessary degrees of freedom from the final mold assembly to facilitate molding.

Molds are required in a large number of manufacturing operations such as metal casting, die making, injection molding, ceramic and polymer processing etc. Molded and cast parts are used extensively because they produce net-shape parts that require minimal secondary operations. On the basis of the number of pieces in a mold, molds can be divided into two piece molds and multi-piece molds. Multi-piece molds refer to molds having more than two pieces. These molds can produce complex parts that cannot be made using two-piece molds. They enable the use of molding for making parts that were previously manufactured using other processes. Since they have more than two pieces, multi-piece molds have more than one parting surface. This enables the mold to be decomposed along different directions and thus can be used to make geometrically complex parts.Sacrificial molds refer to molds that can be destroyed after the part has been produced. They are generally made of low melting point materials such as wax or ABS and are typically destroyed by heating the mold-part assembly. Moreover, the wax molds can be easily machined making them very easy to manufacture at high production rates. Therefore, sacrificial molds can be used to circumvent the disassembly problems that arise in permanent mold casting. Sacrificial multi-piece molds find use in several manufacturing domains. Examples include manufacture of polymer parts and gelcasting of ceramic parts.Our algorithm for automated design of multi-piece sacrificial molds uses a three-step approach. The gross mold is created by subtracting the part from a large rectangular block that completely encloses the part. The three steps of the mold design algorithm are listed below.Decomposition to Solve Accessibility Problems: First, a feature-based decomposition of the mold is done to generate individual mold components for each of the primitives constituting the part. All decompositions are performed along planar faces. Second, once the feature-based decomposition is completed, some of the individual mold components are further decomposed to eliminate concave edges that are not accessible to non-zero diameter milling tools.Combining Mold Components to Reduce Manufacturing Cost: Once the decomposition has been completed, some of the individual mold components may be combined if the resulting mold component is completely accessible. The list of candidate combinations consists of all pairs of mold components that share a common planar face. Among them, only valid combinations are performed. The validation check is guided by a set of rules to ensure the accessibility of the composite mold components resulting from combinations.Addition of Assembly Features: Once the mold combination is completed, assembly features are added to the mold components in the mold assembly.There are a number of potential benefits of automating the design of multi-piece molds. The principal benefits are enumerated below.Mold design is a laborious process that requires significant time from the mold designer. This is aggravated in the case of multi-piece molds. Automated mold design significantly reduces the mold design time.This approach allows us to manufacture parts that could not be produced earlier using two-piece molds. Thus it expands the design space for parts that can be produced using casting processes such as gelcasting and polyurethane manufacturing.Since this approach automatically produces solid models of mold components, it can be integrated with CAM systems to generate the cutter path plans for manufacturing the individual mold components. Thus an integrated system can be developed that can simultaneously design and generate the cutter path plans for manufacturing the individual mold components in a mold assembly.