{"title":"Performance Analysis and Comparison of Different Elliptic Curves on Smart Cards","authors":"Petr Dzurenda, Sara Ricci, J. Hajny, L. Malina","doi":"10.1109/PST.2017.00050","DOIUrl":"https://doi.org/10.1109/PST.2017.00050","url":null,"abstract":"Elliptic curves are very often used in the cryptographic protocol design due to their memory efficiency and useful features, such as the bilinear pairing support. However, in many cryptographic papers, elliptic curves are used as a black box, without deeper consideration of their mathematical properties and, even more importantly, without considering implementation implications. As a consequence, novel cryptographic schemes are being published without any real chance of implementation on constrained devices due to their lack of support of basic EC operations like point addition or scalar point multiplication. This paper provides the necessary theoretical overview of main forms of elliptic curves, in particular considering their computational and memory complexity. Next, all major platforms of programmable smart cards are evaluated with respect to EC support and the performance of basic arithmetic operations is assessed using benchmarks. Finally, the evaluation of the implementations of ECC schemes, such as ECDH and ECDSA, is presented.","PeriodicalId":405887,"journal":{"name":"2017 15th Annual Conference on Privacy, Security and Trust (PST)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122934843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Shadan Ghaffaripour, F. Younis, Hoi Ting Poon, A. Miri
{"title":"An Analysis of the Security of Compressed Sensing Using an Artificial Neural Network","authors":"Shadan Ghaffaripour, F. Younis, Hoi Ting Poon, A. Miri","doi":"10.1109/PST.2017.00052","DOIUrl":"https://doi.org/10.1109/PST.2017.00052","url":null,"abstract":"Compressed sensing (CS) schemes have been used in a wide number of applications in practice. Recently, they have been proposed for use in encryption algorithms because of their properties. In this paper, we present an empirical security analysis of compressed sensing-based encryption. Using a neural network model, we will show that the security of this type of encryption can be compromised. We consider at least three different scenarios in which an attack could occur causing partial information about the plaintext to be revealed without knowledge of the CS secret key.","PeriodicalId":405887,"journal":{"name":"2017 15th Annual Conference on Privacy, Security and Trust (PST)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121363021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Smartphone Continuous Authentication Using Deep Learning Autoencoders","authors":"Mario Parreño Centeno, A. Moorsel, S. Castruccio","doi":"10.1109/PST.2017.00026","DOIUrl":"https://doi.org/10.1109/PST.2017.00026","url":null,"abstract":"Continuous authentication is receiving increased attention from providers of on-line services, particularly due to the ability of mobile apps to collect user-specific sensor data. However, the approaches proposed so far are either not accurate enough to provide a high-quality user experience or restricted by engineering challenges to capture data continuously. In this paper, we propose an approach based on a deep learning autoencoder, which achieves an equal error rate as low as 2:2% in tested real-world scenarios. The suggested system only relies on accelerometer data and does not require a high number of features, therefore reducing the computational burden. We discuss the balance between the number of dimensional features and the re-authentication time, which decreases as the number of dimensions increases. We also discuss parameter selection for real-world scenarios e.g. depth of the architecture, time elapsed before re-building the model and length of the training dataset and possible approaches to find the optimal trade-off between accuracy and usability required for each particular context.","PeriodicalId":405887,"journal":{"name":"2017 15th Annual Conference on Privacy, Security and Trust (PST)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115047338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Automated Static Analysis and Classification of Android Malware using Permission and API Calls Models","authors":"Anastasia Skovoroda, D. Gamayunov","doi":"10.1109/PST.2017.00036","DOIUrl":"https://doi.org/10.1109/PST.2017.00036","url":null,"abstract":"In this paper we propose a heuristic approach to static analysis of Android applications based on matching suspicious applications with the predefined malware models. Static models are built from Android capabilities and Android Framework API call chains used by the application. All of the analysis steps and model construction are fully automated. Therefore, the method can be easily deployed as one of the automated checks provided by mobile application marketplaces or other interested organizations. Using the proposed method we analyzed the Drebin and ISCX malware collections in order to find possible relationships and dependencies between samples in collections, and a large fraction of Google Play apps collected between 2013 and 2016 representing benign data. Analysis results show that a combination of relatively simple static features represented by permissions and API call chains is enough to perform binary classification between malware and benign apps, and even find the corresponding malware family, with an appropriate false positive rate of about 3% (less than 1% in case of filtering adware). Malware collections exploration results show that Android malware rarely uses obfuscation or encryption techniques to make static analysis more difficult, which is quite the opposite of what we see in the case of the 'Wintel' endpoint platform family. We also provide the experiment-based comparison with the previously proposed state-of-the-art Android malware detection method adagio.","PeriodicalId":405887,"journal":{"name":"2017 15th Annual Conference on Privacy, Security and Trust (PST)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128355895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Privacy-Preserving Multi-Party Bartering Secure Against Active Adversaries","authors":"S. Wüller, Ulrike Meyer, S. Wetzel","doi":"10.1109/PST.2017.00032","DOIUrl":"https://doi.org/10.1109/PST.2017.00032","url":null,"abstract":"A majority of electronic bartering transactions is carried out via online platforms. Typically, these platforms require users to disclose sensitive information about their trade capabilities which might restrict their room for negotiation. It is in this context that we propose a novel decentralized and privacypreserving bartering protocol for multiple parties that offers the same privacy guarantees as provided by traditional bartering and by cash payments. The proposed protocol is even secure against an active attacker who controls a majority of colluding parties.","PeriodicalId":405887,"journal":{"name":"2017 15th Annual Conference on Privacy, Security and Trust (PST)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131725505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}