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2021-08-18
Oda, Maya, Ueno, Rei, Inoue, Akiko, Minematsu, Kazuhiko, Homma, Naofumi.  2020.  PMAC++: Incremental MAC Scheme Adaptable to Lightweight Block Ciphers. 2020 IEEE International Symposium on Circuits and Systems (ISCAS). :1—4.
This paper presents a new incremental parallelizable message authentication code (MAC) scheme adaptable to lightweight block ciphers for memory integrity verification. The highlight of the proposed scheme is to achieve both incremental update capability and sufficient security bound with lightweight block ciphers, which is a novel feature. We extend the conventional parallelizable MAC to realize the incremental update capability while keeping the original security bound. We prove that a comparable security bound can be obtained even if this change is incorporated. We also present a hardware architecture for the proposed MAC scheme with lightweight block ciphers and demonstrate the effectiveness through FPGA implementation. The evaluation results indicate that the proposed MAC hardware achieves 3.4 times improvement in the latency-area product for the tag update compared with the conventional MAC.
2021-07-27
Shabbir, Mudassir, Li, Jiani, Abbas, Waseem, Koutsoukos, Xenofon.  2020.  Resilient Vector Consensus in Multi-Agent Networks Using Centerpoints. 2020 American Control Conference (ACC). :4387–4392.
In this paper, we study the resilient vector consensus problem in multi-agent networks and improve resilience guarantees of existing algorithms. In resilient vector consensus, agents update their states, which are vectors in ℝd, by locally interacting with other agents some of which might be adversarial. The main objective is to ensure that normal (non-adversarial) agents converge at a common state that lies in the convex hull of their initial states. Currently, resilient vector consensus algorithms, such as approximate distributed robust convergence (ADRC) are based on the idea that to update states in each time step, every normal node needs to compute a point that lies in the convex hull of its normal neighbors' states. To compute such a point, the idea of Tverberg partition is typically used, which is computationally hard. Approximation algorithms for Tverberg partition negatively impact the resilience guarantees of consensus algorithm. To deal with this issue, we propose to use the idea of centerpoint, which is an extension of median in higher dimensions, instead of Tverberg partition. We show that the resilience of such algorithms to adversarial nodes is improved if we use the notion of centerpoint. Furthermore, using centerpoint provides a better characterization of the necessary and sufficient conditions guaranteeing resilient vector consensus. We analyze these conditions in two, three, and higher dimensions separately. We also numerically evaluate the performance of our approach.
2021-07-08
Li, Yan.  2020.  User Privacy Protection Technology of Tennis Match Live Broadcast from Media Cloud Platform Based on AES Encryption Algorithm. 2020 IEEE 3rd International Conference on Information Systems and Computer Aided Education (ICISCAE). :267—269.
With the improvement of the current Internet software and hardware performance, cloud storage has become one of the most widely used applications. This paper proposes a user privacy protection algorithm suitable for tennis match live broadcast from media cloud platform. Through theoretical and experimental verification, this algorithm can better protect the privacy of users in the live cloud platform. This algorithm is a ciphertext calculation algorithm based on data blocking. Firstly, plaintext data are grouped, then AES ciphertext calculation is performed on each group of plaintext data simultaneously and respectively, and finally ciphertext data after grouping encryption is spliced to obtain final ciphertext data. Experimental results show that the algorithm has the characteristics of large key space, high execution efficiency, ciphertext statistics and good key sensitivity.
2021-05-26
Zhengbo, Chen, Xiu, Liu, Yafei, Xing, Miao, Hu, Xiaoming, Ju.  2020.  Markov Encrypted Data Prefetching Model Based On Attribute Classification. 2020 5th International Conference on Computer and Communication Systems (ICCCS). :54—59.

In order to improve the buffering performance of the data encrypted by CP-ABE (ciphertext policy attribute based encryption), this paper proposed a Markov prefetching model based on attribute classification. The prefetching model combines the access strategy of CP-ABE encrypted file, establishes the user relationship network according to the attribute value of the user, classifies the user by the modularity-based community partitioning algorithm, and establishes a Markov prefetching model based on attribute classification. In comparison with the traditional Markov prefetching model and the classification-based Markov prefetching model, the attribute-based Markov prefetching model is proposed in this paper has higher prefetch accuracy and coverage.

2021-05-25
Santos, Bernardo, Dzogovic, Bruno, Feng, Boning, Jacot, Niels, Do, Van Thuan, Do, Thanh Van.  2020.  Improving Cellular IoT Security with Identity Federation and Anomaly Detection. 2020 5th International Conference on Computer and Communication Systems (ICCCS). :776—780.

As we notice the increasing adoption of Cellular IoT solutions (smart-home, e-health, among others), there are still some security aspects that can be improved as these devices can suffer various types of attacks that can have a high-impact over our daily lives. In order to avoid this, we present a multi-front security solution that consists on a federated cross-layered authentication mechanism, as well as a machine learning platform with anomaly detection techniques for data traffic analysis as a way to study devices' behavior so it can preemptively detect attacks and minimize their impact. In this paper, we also present a proof-of-concept to illustrate the proposed solution and showcase its feasibility, as well as the discussion of future iterations that will occur for this work.

2021-03-22
Li, Y., Zhou, W., Wang, H..  2020.  F-DPC: Fuzzy Neighborhood-Based Density Peak Algorithm. IEEE Access. 8:165963–165972.
Clustering is a concept in data mining, which divides a data set into different classes or clusters according to a specific standard, making the similarity of data objects in the same cluster as large as possible. Clustering by fast search and find of density peaks (DPC) is a novel clustering algorithm based on density. It is simple and novel, only requiring fewer parameters to achieve better clustering effect, without the requirement for iterative solution. And it has expandability and can detect the clustering of any shape. However, DPC algorithm still has some defects, such as it employs the clear neighborhood relations to calculate local density, so it cannot identify the neighborhood membership of different values of points from the distance of points and It is impossible to accurately cluster the data of the multi-density peak. The fuzzy neighborhood density peak clustering algorithm is proposed for this shortcoming (F-DPC): novel local density is defined by the fuzzy neighborhood relationship. The fuzzy set theory can be used to make the fuzzy neighborhood function of local density more sensitive, so that the clustering for data set of various shapes and densities is more robust. Experiments show that the algorithm has high accuracy and robustness.
2021-02-22
Bashyam, K. G. Renga, Vadhiyar, S..  2020.  Fast Scalable Approximate Nearest Neighbor Search for High-dimensional Data. 2020 IEEE International Conference on Cluster Computing (CLUSTER). :294–302.
K-Nearest Neighbor (k-NN) search is one of the most commonly used approaches for similarity search. It finds extensive applications in machine learning and data mining. This era of big data warrants efficiently scaling k-NN search algorithms for billion-scale datasets with high dimensionality. In this paper, we propose a solution towards this end where we use vantage point trees for partitioning the dataset across multiple processes and exploit an existing graph-based sequential approximate k-NN search algorithm called HNSW (Hierarchical Navigable Small World) for searching locally within a process. Our hybrid MPI-OpenMP solution employs techniques including exploiting MPI one-sided communication for reducing communication times and partition replication for better load balancing across processes. We demonstrate computation of k-NN for 10,000 queries in the order of seconds using our approach on 8000 cores on a dataset with billion points in an 128-dimensional space. We also show 10X speedup over a completely k-d tree-based solution for the same dataset, thus demonstrating better suitability of our solution for high dimensional datasets. Our solution shows almost linear strong scaling.
2021-02-15
Bisht, K., Deshmukh, M..  2020.  Encryption algorithm based on knight’s tour and n-neighbourhood addition. 2020 7th International Conference on Signal Processing and Integrated Networks (SPIN). :31–36.
This paper presents a new algorithm for image encryption by extending the Knight's Tour Problem (KTP). The idea behind the proposed algorithm is to generate a Knight Tour (KT) matrix (m,n) and then divide the image according to the size of knight tour matrix into several sub matrices. Finally, apply n-neighborhood addition modulo encryption algorithm according to the solution of KT matrix over each m × n partition of the image. The proposed algorithm provides image encryption without using the cover images. Results obtained from experiments have shown that the proposed algorithm is efficient, simple and does not disclose any information from encrypted image.
2021-01-28
Wang, N., Song, H., Luo, T., Sun, J., Li, J..  2020.  Enhanced p-Sensitive k-Anonymity Models for Achieving Better Privacy. 2020 IEEE/CIC International Conference on Communications in China (ICCC). :148—153.

To our best knowledge, the p-sensitive k-anonymity model is a sophisticated model to resist linking attacks and homogeneous attacks in data publishing. However, if the distribution of sensitive values is skew, the model is difficult to defend against skew attacks and even faces sensitive attacks. In practice, the privacy requirements of different sensitive values are not always identical. The “one size fits all” unified privacy protection level may cause unnecessary information loss. To address these problems, the paper quantifies privacy requirements with the concept of IDF and concerns more about sensitive groups. Two enhanced anonymous models with personalized protection characteristic, that is, (p,αisg) -sensitive k-anonymity model and (pi,αisg)-sensitive k-anonymity model, are then proposed to resist skew attacks and sensitive attacks. Furthermore, two clustering algorithms with global search and local search are designed to implement our models. Experimental results show that the two enhanced models have outstanding advantages in better privacy at the expense of a little data utility.

Zhang, M., Wei, T., Li, Z., Zhou, Z..  2020.  A service-oriented adaptive anonymity algorithm. 2020 39th Chinese Control Conference (CCC). :7626—7631.

Recently, a large amount of research studies aiming at the privacy-preserving data publishing have been conducted. We find that most K-anonymity algorithms fail to consider the characteristics of attribute values distribution in data and the contribution value differences in quasi-identifier attributes when service-oriented. In this paper, the importance of distribution characteristics of attribute values and the differences in contribution value of quasi-identifier attributes to anonymous results are illustrated. In order to maximize the utility of released data, a service-oriented adaptive anonymity algorithm is proposed. We establish a model of reaction dispersion degree to quantify the characteristics of attribute value distribution and introduce the concept of utility weight related to the contribution value of quasi-identifier attributes. The priority coefficient and the characterization coefficient of partition quality are defined to optimize selection strategies of dimension and splitting value in anonymity group partition process adaptively, which can reduce unnecessary information loss so as to further improve the utility of anonymized data. The rationality and validity of the algorithm are verified by theoretical analysis and multiple experiments.

2020-12-28
Marichamy, V. S., Natarajan, V..  2020.  A Study of Big Data Security on a Partitional Clustering Algorithm with Perturbation Technique. 2020 International Conference on Smart Electronics and Communication (ICOSEC). :482—486.

Partitional Clustering Algorithm (PCA) on the Hadoop Distributed File System is to perform big data securities using the Perturbation Technique is the main idea of the proposed work. There are numerous clustering methods available that are used to categorize the information from the big data. PCA discovers the cluster based on the initial partition of the data. In this approach, it is possible to develop a security safeguarding of data that is impoverished to allow the calculations and communication. The performances were analyzed on Health Care database under the studies of various parameters like precision, accuracy, and F-score measure. The outcome of the results is to demonstrate that this method is used to decrease the complication in preserving privacy and better accuracy than that of the existing techniques.

2020-08-13
Cheng, Chen, Xiaoli, Liu, Linfeng, Wei, Longxin, Lin, Xiaofeng, Wu.  2019.  Algorithm for k-anonymity based on ball-tree and projection area density partition. 2019 14th International Conference on Computer Science Education (ICCSE). :972—975.

K-anonymity is a popular model used in microdata publishing to protect individual privacy. This paper introduces the idea of ball tree and projection area density partition into k-anonymity algorithm.The traditional kd-tree implements the division by forming a super-rectangular, but the super-rectangular has the area angle, so it cannot guarantee that the records on the corner are most similar to the records in this area. In this paper, the super-sphere formed by the ball-tree is used to address this problem. We adopt projection area density partition to increase the density of the resulting recorded points. We implement our algorithm with the Gotrack dataset and the Adult dataset in UCI. The experimentation shows that the k-anonymity algorithm based on ball-tree and projection area density partition, obtains more anonymous groups, and the generalization rate is lower. The smaller the K is, the more obvious the result advantage is. The result indicates that our algorithm can make data usability even higher.

2020-06-08
Huang, Jiamin, Lu, Yueming, Guo, Kun.  2019.  A Hybrid Packet Classification Algorithm Based on Hash Table and Geometric Space Partition. 2019 IEEE Fourth International Conference on Data Science in Cyberspace (DSC). :587–592.
The emergence of integrated space-ground network (ISGN), with more complex network conditions compared with tradition network, requires packet classification to achieve high performance. Packet classification plays an important role in the field of network security. Although several existing classification schemes have been proposed recently to improve classification performance, the performance of these schemes is unable to meet the high-speed packet classification requirement in ISGN. To tackle this problem, a hybrid packet classification algorithm based on hash table and geometric space partition (HGSP) is proposed in this paper. HGSP falls into two sections: geometric space partition and hash matching. To improve the classification speed under the same accuracy, a parallel structure of hash table is designed to match the huge packets for classifying. The experimental results demonstrate that the matching time of HGSP algorithm is reduced by 40%-70% compared with traditional Hicuts algorithm. Particularly, with the growth of ruleset, the advantage of HGSP algorithm will become more obvious.
2020-05-11
Anand Sukumar, J V, Pranav, I, Neetish, MM, Narayanan, Jayasree.  2018.  Network Intrusion Detection Using Improved Genetic k-means Algorithm. 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI). :2441–2446.
Internet is a widely used platform nowadays by people across the globe. This has led to the advancement in science and technology. Many surveys show that network intrusion has registered a consistent increase and lead to personal privacy theft and has become a major platform for attack in the recent years. Network intrusion is any unauthorized activity on a computer network. Hence there is a need to develop an effective intrusion detection system. In this paper we acquaint an intrusion detection system that uses improved genetic k-means algorithm(IGKM) to detect the type of intrusion. This paper also shows a comparison between an intrusion detection system that uses the k-means++ algorithm and an intrusion detection system that uses IGKM algorithm while using smaller subset of kdd-99 dataset with thousand instances and the KDD-99 dataset. The experiment shows that the intrusion detection that uses IGKM algorithm is more accurate when compared to k-means++ algorithm.
2020-01-27
Fuchs, Caro, Spolaor, Simone, Nobile, Marco S., Kaymak, Uzay.  2019.  A Swarm Intelligence Approach to Avoid Local Optima in Fuzzy C-Means Clustering. 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). :1–6.
Clustering analysis is an important computational task that has applications in many domains. One of the most popular algorithms to solve the clustering problem is fuzzy c-means, which exploits notions from fuzzy logic to provide a smooth partitioning of the data into classes, allowing the possibility of multiple membership for each data sample. The fuzzy c-means algorithm is based on the optimization of a partitioning function, which minimizes inter-cluster similarity. This optimization problem is known to be NP-hard and it is generally tackled using a hill climbing method, a local optimizer that provides acceptable but sub-optimal solutions, since it is sensitive to initialization and tends to get stuck in local optima. In this work we propose an alternative approach based on the swarm intelligence global optimization method Fuzzy Self-Tuning Particle Swarm Optimization (FST-PSO). We solve the fuzzy clustering task by optimizing fuzzy c-means' partitioning function using FST-PSO. We show that this population-based metaheuristics is more effective than hill climbing, providing high quality solutions with the cost of an additional computational complexity. It is noteworthy that, since this particle swarm optimization algorithm is self-tuning, the user does not have to specify additional hyperparameters for the optimization process.
2019-09-23
Suriarachchi, I., Withana, S., Plale, B..  2018.  Big Provenance Stream Processing for Data Intensive Computations. 2018 IEEE 14th International Conference on e-Science (e-Science). :245–255.
In the business and research landscape of today, data analysis consumes public and proprietary data from numerous sources, and utilizes any one or more of popular data-parallel frameworks such as Hadoop, Spark and Flink. In the Data Lake setting these frameworks co-exist. Our earlier work has shown that data provenance in Data Lakes can aid with both traceability and management. The sheer volume of fine-grained provenance generated in a multi-framework application motivates the need for on-the-fly provenance processing. We introduce a new parallel stream processing algorithm that reduces fine-grained provenance while preserving backward and forward provenance. The algorithm is resilient to provenance events arriving out-of-order. It is evaluated using several strategies for partitioning a provenance stream. The evaluation shows that the parallel algorithm performs well in processing out-of-order provenance streams, with good scalability and accuracy.
2018-09-28
Alnemari, A., Romanowski, C. J., Raj, R. K..  2017.  An Adaptive Differential Privacy Algorithm for Range Queries over Healthcare Data. 2017 IEEE International Conference on Healthcare Informatics (ICHI). :397–402.

Differential privacy is an approach that preserves patient privacy while permitting researchers access to medical data. This paper presents mechanisms proposed to satisfy differential privacy while answering a given workload of range queries. Representing input data as a vector of counts, these methods partition the vector according to relationships between the data and the ranges of the given queries. After partitioning the vector into buckets, the counts of each bucket are estimated privately and split among the bucket's positions to answer the given query set. The performance of the proposed method was evaluated using different workloads over several attributes. The results show that partitioning the vector based on the data can produce more accurate answers, while partitioning the vector based on the given workload improves privacy. This paper's two main contributions are: (1) improving earlier work on partitioning mechanisms by building a greedy algorithm to partition the counts' vector efficiently, and (2) its adaptive algorithm considers the sensitivity of the given queries before providing results.

2018-06-11
Yang, C., Li, Z., Qu, W., Liu, Z., Qi, H..  2017.  Grid-Based Indexing and Search Algorithms for Large-Scale and High-Dimensional Data. 2017 14th International Symposium on Pervasive Systems, Algorithms and Networks 2017 11th International Conference on Frontier of Computer Science and Technology 2017 Third International Symposium of Creative Computing (ISPAN-FCST-ISCC). :46–51.

The rapid development of Internet has resulted in massive information overloading recently. These information is usually represented by high-dimensional feature vectors in many related applications such as recognition, classification and retrieval. These applications usually need efficient indexing and search methods for such large-scale and high-dimensional database, which typically is a challenging task. Some efforts have been made and solved this problem to some extent. However, most of them are implemented in a single machine, which is not suitable to handle large-scale database.In this paper, we present a novel data index structure and nearest neighbor search algorithm implemented on Apache Spark. We impose a grid on the database and index data by non-empty grid cells. This grid-based index structure is simple and easy to be implemented in parallel. Moreover, we propose to build a scalable KNN graph on the grids, which increase the efficiency of this index structure by a low cost in parallel implementation. Finally, experiments are conducted in both public databases and synthetic databases, showing that the proposed methods achieve overall high performance in both efficiency and accuracy.

2018-04-02
Wei, R., Shen, H., Tian, H..  2017.  An Improved (k,p,l)-Anonymity Method for Privacy Preserving Collaborative Filtering. GLOBECOM 2017 - 2017 IEEE Global Communications Conference. :1–6.

Collaborative Filtering (CF) is a successful technique that has been implemented in recommender systems and Privacy Preserving Collaborative Filtering (PPCF) aroused increasing concerns of the society. Current solutions mainly focus on cryptographic methods, obfuscation methods, perturbation methods and differential privacy methods. But these methods have some shortcomings, such as unnecessary computational cost, lower data quality and hard to calibrate the magnitude of noise. This paper proposes a (k, p, I)-anonymity method that improves the existing k-anonymity method in PPCF. The method works as follows: First, it applies Latent Factor Model (LFM) to reduce matrix sparsity. Then it improves Maximum Distance to Average Vector (MDAV) microaggregation algorithm based on importance partitioning to increase homogeneity among records in each group which can retain better data quality and (p, I)-diversity model where p is attacker's prior knowledge about users' ratings and I is the diversity among users in each group to improve the level of privacy preserving. Theoretical and experimental analyses show that our approach ensures a higher level of privacy preserving based on lower information loss.

2018-02-27
Zhao, J..  2017.  Composition Properties of Bayesian Differential Privacy. 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC). :1–5.

Differential privacy is a rigorous privacy standard that has been applied to a range of data analysis tasks. To broaden the application scenarios of differential privacy when data records have dependencies, the notion of Bayesian differential privacy has been recently proposed. However, it is unknown whether Bayesian differential privacy preserves three nice properties of differential privacy: sequential composability, parallel composability, and post-processing. In this paper, we provide an affirmative answer to this question; i.e., Bayesian differential privacy still have these properties. The idea behind sequential composability is that if we have m algorithms Y1, Y2,łdots, Ym, where Y$\mathscrl$ is independently $ε\mathscrl$-Bayesian differential private for $\mathscrl$ = 1,2,łdots, m, then by feeding the result of Y1 into Y2, the result of Y2 into Y3, and so on, we will finally have an $Σ$m$\mathscrl$=;1 $ε\mathscrl$-Bayesian differential private algorithm. For parallel composability, we consider the situation where a database is partitioned into m disjoint subsets. The $\mathscrl$-th subset is input to a Bayesian differential private algorithm Y$\mathscrl$, for $\mathscrl$= 1, 2,łdots, m. Then the parallel composition of Y1, Y2,łdots, Ym will be maxm$\mathscrl$=;1=1 $ε\mathscrl$-Bayesian differential private. The postprocessing property means that a data analyst, without additional knowledge abo- t the private database, cannot compute a function of the output of a Bayesian differential private algorithm and reduce its privacy guarantee.

2018-02-21
Pak, W., Choi, Y. J..  2017.  High Performance and High Scalable Packet Classification Algorithm for Network Security Systems. IEEE Transactions on Dependable and Secure Computing. 14:37–49.

Packet classification is a core function in network and security systems; hence, hardware-based solutions, such as packet classification accelerator chips or Ternary Content Addressable Memory (T-CAM), have been widely adopted for high-performance systems. With the rapid improvement of general hardware architectures and growing popularity of multi-core multi-threaded processors, software-based packet classification algorithms are attracting considerable attention, owing to their high flexibility in satisfying various industrial requirements for security and network systems. For high classification speed, these algorithms internally use large tables, whose size increases exponentially with the ruleset size; consequently, they cannot be used with a large rulesets. To overcome this problem, we propose a new software-based packet classification algorithm that simultaneously supports high scalability and fast classification performance by merging partition decision trees in a search table. While most partitioning-based packet classification algorithms show good scalability at the cost of low classification speed, our algorithm shows very high classification speed, irrespective of the number of rules, with small tables and short table building time. Our test results confirm that the proposed algorithm enables network and security systems to support heavy traffic in the most effective manner.

2018-01-10
Zaman, A. N. K., Obimbo, C., Dara, R. A..  2017.  An improved differential privacy algorithm to protect re-identification of data. 2017 IEEE Canada International Humanitarian Technology Conference (IHTC). :133–138.

In the present time, there has been a huge increase in large data repositories by corporations, governments, and healthcare organizations. These repositories provide opportunities to design/improve decision-making systems by mining trends and patterns from the data set (that can provide credible information) to improve customer service (e.g., in healthcare). As a result, while data sharing is essential, it is an obligation to maintaining the privacy of the data donors as data custodians have legal and ethical responsibilities to secure confidentiality. This research proposes a 2-layer privacy preserving (2-LPP) data sanitization algorithm that satisfies ε-differential privacy for publishing sanitized data. The proposed algorithm also reduces the re-identification risk of the sanitized data. The proposed algorithm has been implemented, and tested with two different data sets. Compared to other existing works, the results obtained from the proposed algorithm show promising performance.

Higuchi, K., Yoshida, M., Tsuji, T., Miyamoto, N..  2017.  Correctness of the routing algorithm for distributed key-value store based on order preserving linear hashing and skip graph. 2017 18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD). :459–464.

In this paper, the correctness of the routing algorithm for the distributed key-value store based on order preserving linear hashing and Skip Graph is proved. In this system, data are divided by linear hashing and Skip Graph is used for overlay network. The routing table of this system is very uniform. Then, short detours can exist in the route of forwarding. By using these detours, the number of hops for the query forwarding is reduced.

2017-12-28
Panetta, J., Filho, P. R. P. S., Laranjeira, L. A. F., Teixeira, C. A..  2017.  Scalability of CPU and GPU Solutions of the Prime Elliptic Curve Discrete Logarithm Problem. 2017 29th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD). :33–40.

Elliptic curve asymmetric cryptography has achieved increased popularity due to its capability of providing comparable levels of security as other existing cryptographic systems while requiring less computational work. Pollard Rho and Parallel Collision Search, the fastest known sequential and parallel algorithms for breaking this cryptographic system, have been successfully applied over time to break ever-increasing bit-length system instances using implementations heavily optimized for the available hardware. This work presents portable, general implementations of a Parallel Collision Search based solution for prime elliptic curve asymmetric cryptographic systems that use publicly available big integer libraries and make no assumption on prime curve properties. It investigates which bit-length keys can be broken in reasonable time by a user that has access to a state of the art, public HPC equipment with CPUs and GPUs. The final implementation breaks a 79-bit system in about two hours using 80 GPUs and 94-bits system in about 15 hours using 256 GPUs. Extensive experimentation investigates scalability of CPU, GPU and CPU+GPU runs. The discussed results indicate that speed-up is not a good metric for parallel scalability. This paper proposes and evaluates a new metric that is better suited for this task.