{"title":"Proceedings of the 2nd International Workshop on Multimedia Privacy and Security","authors":"","doi":"10.1145/3267357","DOIUrl":"https://doi.org/10.1145/3267357","url":null,"abstract":"","PeriodicalId":263315,"journal":{"name":"Proceedings of the 2nd International Workshop on Multimedia Privacy and Security","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126392821","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}
Personal Voice Assistants (PVAs) such as the Amazon Echo are commonplace and it is now likely to always be in range of at least one PVA. Although the devices are very helpful they are also continuously monitoring conversations. When a PVA detects a wake word, the immediately following conversation is recorded and transported to a cloud system for further analysis. In this paper we investigate an active protection mechanism against PVAs: reactive jamming. A Protection Jamming Device (PJD) is employed to observe conversations. Upon detection of a PVA wake word the PJD emits an acoustic jamming signal. The PJD must detect the wake word faster than the PVA such that the jamming signal still prevents wake word detection by the PVA. The paper presents an evaluation of the effectiveness of different jamming signals. We quantify the impact of jamming signal and wake word overlap on jamming success. Furthermore, we quantify the jamming false positive rate in dependence of the overlap. Our evaluation shows that a 100% jamming success can be achieved with an overlap of at least 60% with a negligible false positive rate. Thus, reactive jamming of PVAs is feasible without creating a system perceived as a noise nuisance.
{"title":"Towards Reactive Acoustic Jamming for Personal Voice Assistants","authors":"Peng Cheng, I. E. Bagci, Jeff Yan, U. Roedig","doi":"10.1145/3267357.3267359","DOIUrl":"https://doi.org/10.1145/3267357.3267359","url":null,"abstract":"Personal Voice Assistants (PVAs) such as the Amazon Echo are commonplace and it is now likely to always be in range of at least one PVA. Although the devices are very helpful they are also continuously monitoring conversations. When a PVA detects a wake word, the immediately following conversation is recorded and transported to a cloud system for further analysis. In this paper we investigate an active protection mechanism against PVAs: reactive jamming. A Protection Jamming Device (PJD) is employed to observe conversations. Upon detection of a PVA wake word the PJD emits an acoustic jamming signal. The PJD must detect the wake word faster than the PVA such that the jamming signal still prevents wake word detection by the PVA. The paper presents an evaluation of the effectiveness of different jamming signals. We quantify the impact of jamming signal and wake word overlap on jamming success. Furthermore, we quantify the jamming false positive rate in dependence of the overlap. Our evaluation shows that a 100% jamming success can be achieved with an overlap of at least 60% with a negligible false positive rate. Thus, reactive jamming of PVAs is feasible without creating a system perceived as a noise nuisance.","PeriodicalId":263315,"journal":{"name":"Proceedings of the 2nd International Workshop on Multimedia Privacy and Security","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129262370","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}
M. Steinebach, A. Ester, Huajian Liu, Sascha Zmuzinksi
The rise of social networks during the last 10 years has created a situation in which up to 100 million new images and photographs are uploaded and shared by users every day. This environment poses a ideal background for those who wish to communicate covertly by the use of steganography. It also creates a new set of challenges for steganalysts, who have to shift their field of work away from a purely scientific laboratory environment and into a diverse real-world scenario, while at the same time having to deal with entirely new problems, such as the detection of steganographic channels or the impact that even a low false positive rate has when investigating the millions of images which are shared every day on social networks. We evaluate how to address these challenges with traditional steganographic and statistical methods, rather then using high performance computing and machine learning. By the double embedding attack on the well-known F5 steganographic algorithm we achieve a false positive rate well below known attacks.
{"title":"Double Embedding Steganalysis: Steganalysis with Low False Positive Rate","authors":"M. Steinebach, A. Ester, Huajian Liu, Sascha Zmuzinksi","doi":"10.1145/3267357.3267364","DOIUrl":"https://doi.org/10.1145/3267357.3267364","url":null,"abstract":"The rise of social networks during the last 10 years has created a situation in which up to 100 million new images and photographs are uploaded and shared by users every day. This environment poses a ideal background for those who wish to communicate covertly by the use of steganography. It also creates a new set of challenges for steganalysts, who have to shift their field of work away from a purely scientific laboratory environment and into a diverse real-world scenario, while at the same time having to deal with entirely new problems, such as the detection of steganographic channels or the impact that even a low false positive rate has when investigating the millions of images which are shared every day on social networks. We evaluate how to address these challenges with traditional steganographic and statistical methods, rather then using high performance computing and machine learning. By the double embedding attack on the well-known F5 steganographic algorithm we achieve a false positive rate well below known attacks.","PeriodicalId":263315,"journal":{"name":"Proceedings of the 2nd International Workshop on Multimedia Privacy and Security","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133536664","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}
This paper describes how a privacy policy framework can be extended with timing information to not only decide if requests for data are allowed at a given point in time, but also to decide for how long such permission is granted. Augmenting policy decisions with expiration information eliminates the need to reason about access permissions prior to every individual data access operation. This facilitates the application of privacy policy frameworks to protect multimedia streaming data where repeated re-computations of policy decisions are not a viable option. We show how timing information can be integrated into an existing declarative privacy policy framework. In particular, we discuss how to obtain valid expiration information in the presence of complex sets of policies with potentially interacting policies and varying timing information.
{"title":"Expiring Decisions for Stream-based Data Access in a Declarative Privacy Policy Framework","authors":"Karsten Martiny, G. Denker","doi":"10.1145/3267357.3267361","DOIUrl":"https://doi.org/10.1145/3267357.3267361","url":null,"abstract":"This paper describes how a privacy policy framework can be extended with timing information to not only decide if requests for data are allowed at a given point in time, but also to decide for how long such permission is granted. Augmenting policy decisions with expiration information eliminates the need to reason about access permissions prior to every individual data access operation. This facilitates the application of privacy policy frameworks to protect multimedia streaming data where repeated re-computations of policy decisions are not a viable option. We show how timing information can be integrated into an existing declarative privacy policy framework. In particular, we discuss how to obtain valid expiration information in the presence of complex sets of policies with potentially interacting policies and varying timing information.","PeriodicalId":263315,"journal":{"name":"Proceedings of the 2nd International Workshop on Multimedia Privacy and Security","volume":"2018 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114448573","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}
Patricia Arias Cabarcos, F. Almenárez, Daniel Díaz Sánchez, Andrés Marín López
Our devices and interactions in a world where physical and digital realities are more and more blended, generate a continuum of multimedia data that needs to be stored, shared and processed to provide services that enrich our daily lives. Cloud computing plays a key role in these tasks, dissolving resource allocation and computational boundaries, but it also requires advanced security mechanisms to protect the data and provide privacy guarantees. Therefore, security assurance must be evaluated before offloading tasks to a cloud provider, a process which is currently manual, complex and inadequate for dynamic scenarios. However, though there are many tools for evaluating cloud providers according to quality of service criteria, automated categorization and selection based on risk metrics is still challenging. To address this gap, we present FRiCS, a Framework for Risk-driven Cloud Selection, which contributes with: 1) a set of cloud security metrics and risk-based weighting policies, 2) distributed components for metric extraction and aggregation, and 3) decision-making plugins for ranking and selection. We have implemented the whole system and conducted a case-study validation based on public cloud providers' security data, showing the benefits of the proposed approach.
{"title":"FRiCS: A Framework for Risk-driven Cloud Selection","authors":"Patricia Arias Cabarcos, F. Almenárez, Daniel Díaz Sánchez, Andrés Marín López","doi":"10.1145/3267357.3267362","DOIUrl":"https://doi.org/10.1145/3267357.3267362","url":null,"abstract":"Our devices and interactions in a world where physical and digital realities are more and more blended, generate a continuum of multimedia data that needs to be stored, shared and processed to provide services that enrich our daily lives. Cloud computing plays a key role in these tasks, dissolving resource allocation and computational boundaries, but it also requires advanced security mechanisms to protect the data and provide privacy guarantees. Therefore, security assurance must be evaluated before offloading tasks to a cloud provider, a process which is currently manual, complex and inadequate for dynamic scenarios. However, though there are many tools for evaluating cloud providers according to quality of service criteria, automated categorization and selection based on risk metrics is still challenging. To address this gap, we present FRiCS, a Framework for Risk-driven Cloud Selection, which contributes with: 1) a set of cloud security metrics and risk-based weighting policies, 2) distributed components for metric extraction and aggregation, and 3) decision-making plugins for ranking and selection. We have implemented the whole system and conducted a case-study validation based on public cloud providers' security data, showing the benefits of the proposed approach.","PeriodicalId":263315,"journal":{"name":"Proceedings of the 2nd International Workshop on Multimedia Privacy and Security","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124398401","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}
The internet-of-things (IoT) consists of embedded devices and their networks of communication as they form decentralized frameworks of ubiquitous computing services. Within such decentralized systems the potential for malicious actors to impact the system is significant, with far-reaching consequences. Hence this work addresses the challenge of providing IoT systems engineers with a framework to elicit privacy and security design considerations, specifically for indoor adaptive smart environments. It introduces a new ambient intelligence indoor adaptive environment framework (CORE) which leverages multiple forms of data, and aims to elicit the privacy and security needs of this representative system. This contributes both a new adaptive IoT framework, but also an approach to systematically derive privacy and security design requirements via a combined and modified OCTAVE-Allegro and Privacy-by-Design methodology. This process also informs the future developments and evaluations of the CORE system, toward engineering more secure and private IoT systems.
{"title":"Deriving Privacy and Security Considerations for CORE: An Indoor IoT Adaptive Context Environment","authors":"Alexis Morris, Nadine Lessio","doi":"10.1145/3267357.3267363","DOIUrl":"https://doi.org/10.1145/3267357.3267363","url":null,"abstract":"The internet-of-things (IoT) consists of embedded devices and their networks of communication as they form decentralized frameworks of ubiquitous computing services. Within such decentralized systems the potential for malicious actors to impact the system is significant, with far-reaching consequences. Hence this work addresses the challenge of providing IoT systems engineers with a framework to elicit privacy and security design considerations, specifically for indoor adaptive smart environments. It introduces a new ambient intelligence indoor adaptive environment framework (CORE) which leverages multiple forms of data, and aims to elicit the privacy and security needs of this representative system. This contributes both a new adaptive IoT framework, but also an approach to systematically derive privacy and security design requirements via a combined and modified OCTAVE-Allegro and Privacy-by-Design methodology. This process also informs the future developments and evaluations of the CORE system, toward engineering more secure and private IoT systems.","PeriodicalId":263315,"journal":{"name":"Proceedings of the 2nd International Workshop on Multimedia Privacy and Security","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124604660","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}
In this work, we describe the design and implementation of a private-key voice encryption system that is designed to encrypt and decrypt voice communications between two people using lightweight computational devices (such as a Raspberry Pi) that sits between the headset and the communication platform (computer, phone, etc.). The key challenge in this work is designing lightweight encryption algorithms in such a way that even before voice enters the phone/computer platform, voice is encrypted, yet such that modern audio communication channels such as popular VoIP applications (such as Skype, Google Voice, etc.), or mobile communications (GSM, etc.) or other (Radio, etc.) do not filter encrypted voice out as "noise" and voice quality is preserved. Thus, two people with two such devices can communicate securely even if their smart phones and/or computers are compromised. Unlike previous solutions, our proposed work does not rely on special-purpose hardware, nor does it rely on trusting the communication device. It is a standalone solution that can be readily deployed on lightweight commodity hardware. We have tested our solution on two Raspberry Pi models and over a variety of communication channels, where we were able to carry a real-time voice conversation.
在这项工作中，我们描述了一个私钥语音加密系统的设计和实现，该系统旨在加密和解密使用位于耳机和通信平台(计算机，电话等)之间的轻量级计算设备(如树莓派)的两个人之间的语音通信。这项工作的关键挑战是设计轻量级的加密算法，即使在语音进入电话/计算机平台之前，语音也被加密，但这样的现代音频通信渠道，如流行的VoIP应用程序(如Skype, Google voice等)，或移动通信(GSM等)或其他(无线电等)不会将加密的语音作为“噪声”过滤掉，并且语音质量得到保留。因此，两个人拥有两个这样的设备，即使他们的智能手机和/或电脑受到威胁，也可以安全地通信。与以前的解决方案不同，我们提出的工作不依赖于专用硬件，也不依赖于信任通信设备。它是一个独立的解决方案，可以很容易地部署在轻量级商用硬件上。我们已经在两个树莓派模型和各种通信渠道上测试了我们的解决方案，我们能够进行实时语音对话。
{"title":"ETERNAL: Encrypted Transmission With an Error-correcting, Real-time, Noise-resilient Apparatus on Lightweight Devices","authors":"Quinn Grier, B. Falk, Steve Lu, R. Ostrovsky","doi":"10.1145/3267357.3267360","DOIUrl":"https://doi.org/10.1145/3267357.3267360","url":null,"abstract":"In this work, we describe the design and implementation of a private-key voice encryption system that is designed to encrypt and decrypt voice communications between two people using lightweight computational devices (such as a Raspberry Pi) that sits between the headset and the communication platform (computer, phone, etc.). The key challenge in this work is designing lightweight encryption algorithms in such a way that even before voice enters the phone/computer platform, voice is encrypted, yet such that modern audio communication channels such as popular VoIP applications (such as Skype, Google Voice, etc.), or mobile communications (GSM, etc.) or other (Radio, etc.) do not filter encrypted voice out as \"noise\" and voice quality is preserved. Thus, two people with two such devices can communicate securely even if their smart phones and/or computers are compromised. Unlike previous solutions, our proposed work does not rely on special-purpose hardware, nor does it rely on trusting the communication device. It is a standalone solution that can be readily deployed on lightweight commodity hardware. We have tested our solution on two Raspberry Pi models and over a variety of communication channels, where we were able to carry a real-time voice conversation.","PeriodicalId":263315,"journal":{"name":"Proceedings of the 2nd International Workshop on Multimedia Privacy and Security","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132210962","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}
N. Vlajic, Marmara El Masri, Gianluigi M. Riva, Marguerite Barry, Derek Doran
The recent news of a large-scale online tracking campaign involving Facebook users, which gave way to systematic misuse of the collected user-related data, have left millions of people deeply concerned about the state of their online privacy as well as the state of the overall information security in the cyber world. While most to-date revelations pertaining to user tracking are related to websites and social media generally intended for adult online users, relatively little is known about the prevalence of online tracking in websites geared towards children and teens. In this paper, we first provide a brief overview of two laws that seek to protect the privacy of kids and teens online ? the US Children's Online Privacy Act (COPPA) and the EU General Data Protection Regulation (GDPR). Subsequently, we present the results of our study which has looked for potential signs of user tracking in twenty select children-oriented websites in case of a user located in the USA (where COPPA is applicable) as well as a user located in the EU (where GDPR is applicable). The key findings of this study are alarming as they point to overwhelming evidence of widespread and highly covert user tracking in a range of different children-oriented websites. The majority of the discovered tracking is in direct conflict with both COPPA and GDPR, since it is performed without parental consent and by third-party advertising and tracking companies. The results also imply that, relative to their US counterparts, the children residing in the EU may be somewhat less subjected (but are still significantly exposed) to tracking by third-party companies.
{"title":"Online Tracking of Kids and Teens by Means of Invisible Images: COPPA vs. GDPR","authors":"N. Vlajic, Marmara El Masri, Gianluigi M. Riva, Marguerite Barry, Derek Doran","doi":"10.1145/3267357.3267370","DOIUrl":"https://doi.org/10.1145/3267357.3267370","url":null,"abstract":"The recent news of a large-scale online tracking campaign involving Facebook users, which gave way to systematic misuse of the collected user-related data, have left millions of people deeply concerned about the state of their online privacy as well as the state of the overall information security in the cyber world. While most to-date revelations pertaining to user tracking are related to websites and social media generally intended for adult online users, relatively little is known about the prevalence of online tracking in websites geared towards children and teens. In this paper, we first provide a brief overview of two laws that seek to protect the privacy of kids and teens online ? the US Children's Online Privacy Act (COPPA) and the EU General Data Protection Regulation (GDPR). Subsequently, we present the results of our study which has looked for potential signs of user tracking in twenty select children-oriented websites in case of a user located in the USA (where COPPA is applicable) as well as a user located in the EU (where GDPR is applicable). The key findings of this study are alarming as they point to overwhelming evidence of widespread and highly covert user tracking in a range of different children-oriented websites. The majority of the discovered tracking is in direct conflict with both COPPA and GDPR, since it is performed without parental consent and by third-party advertising and tracking companies. The results also imply that, relative to their US counterparts, the children residing in the EU may be somewhat less subjected (but are still significantly exposed) to tracking by third-party companies.","PeriodicalId":263315,"journal":{"name":"Proceedings of the 2nd International Workshop on Multimedia Privacy and Security","volume":"183 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132283402","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}
The integrity and reliability of speech data have been important issues to probative use. Watermarking technologies supplies an alternative solution to guarantee the the authenticity of multiple data besides digital signature. This work proposes a novel digital watermarking based on a reversible compression algorithm with sample scanning to detect tampering in time domain. In order to detect tampering precisely, the digital speech data is divided into length-fixed frames and the content-based hash information of each frame is calculated and embedded into the speech data for verification. Huffman compression algorithm is applied to each four sampling bits from least significant bit in each sample after pulse-code modulation processing to achieve low distortion and high capacity for hiding payload. Experimental experiments on audio quality, detection precision and robustness towards attacks are taken, and the results show the effectiveness of tampering detection with a precision with an error around 0.032 s for a 10 s speech clip. Distortion is imperceptible with an average 22.068 dB for Huffman-based and 24.139 dB for intDCT-based method in terms of signal-to-noise, and with an average MOS 3.478 for Huffman-based and 4.378 for intDCT-based method. The bit error rate (BER) between stego data and attacked stego data in both of time-domain and frequency domain is approximate 28.6% in average, which indicates the robustness of the proposed hiding method.
{"title":"Mechanism and Implementation of Watermarked Sample Scanning Method for Speech Data Tampering Detection","authors":"Xuping Huang","doi":"10.1145/3267357.3267371","DOIUrl":"https://doi.org/10.1145/3267357.3267371","url":null,"abstract":"The integrity and reliability of speech data have been important issues to probative use. Watermarking technologies supplies an alternative solution to guarantee the the authenticity of multiple data besides digital signature. This work proposes a novel digital watermarking based on a reversible compression algorithm with sample scanning to detect tampering in time domain. In order to detect tampering precisely, the digital speech data is divided into length-fixed frames and the content-based hash information of each frame is calculated and embedded into the speech data for verification. Huffman compression algorithm is applied to each four sampling bits from least significant bit in each sample after pulse-code modulation processing to achieve low distortion and high capacity for hiding payload. Experimental experiments on audio quality, detection precision and robustness towards attacks are taken, and the results show the effectiveness of tampering detection with a precision with an error around 0.032 s for a 10 s speech clip. Distortion is imperceptible with an average 22.068 dB for Huffman-based and 24.139 dB for intDCT-based method in terms of signal-to-noise, and with an average MOS 3.478 for Huffman-based and 4.378 for intDCT-based method. The bit error rate (BER) between stego data and attacked stego data in both of time-domain and frequency domain is approximate 28.6% in average, which indicates the robustness of the proposed hiding method.","PeriodicalId":263315,"journal":{"name":"Proceedings of the 2nd International Workshop on Multimedia Privacy and Security","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124328308","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":"Session details: Internet of Things and Cloud-based Services","authors":"Roger A. Hallman","doi":"10.1145/3285940","DOIUrl":"https://doi.org/10.1145/3285940","url":null,"abstract":"","PeriodicalId":263315,"journal":{"name":"Proceedings of the 2nd International Workshop on Multimedia Privacy and Security","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132696273","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}
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