Combinatorial auctions are a research hot spot. They impact people's daily lives in many applications such as spectrum auctions held by the FCC. In such auctions, bidders may want to submit bids for combinations of goods. The challenge is how to protect the privacy of bidding prices and ensure data security in these auctions? To tackle this challenge, we present an approach based on verifiable secret sharing. The approach is to represent the price in the degree of a polynomial; thus the maximum/sum of the degree of two polynomials can be obtained by the degree of the sum/product of the two polynomials based on secret sharing. This protocol hides the information of bidders (bidding price) from the auction servers. The auctioneers can obtain their secret shares from bidders without a secure channel. Since it doesn't need a secure channel, this scheme is more practical and applicable to more scenarios. This scheme provides resistance to collusion attacks, conspiracy attacks, passive attacks and so on. Compared to [11, 12], our proposed scheme provides authentication without increasing the communication cost.
{"title":"Secure Auctions without an Auctioneer via Verifiable Secret Sharing","authors":"Maya Larson, Chun-qiang Hu, Ruinian Li, Wei Li, Xiuzhen Cheng","doi":"10.1145/2757302.2757305","DOIUrl":"https://doi.org/10.1145/2757302.2757305","url":null,"abstract":"Combinatorial auctions are a research hot spot. They impact people's daily lives in many applications such as spectrum auctions held by the FCC. In such auctions, bidders may want to submit bids for combinations of goods. The challenge is how to protect the privacy of bidding prices and ensure data security in these auctions? To tackle this challenge, we present an approach based on verifiable secret sharing. The approach is to represent the price in the degree of a polynomial; thus the maximum/sum of the degree of two polynomials can be obtained by the degree of the sum/product of the two polynomials based on secret sharing. This protocol hides the information of bidders (bidding price) from the auction servers. The auctioneers can obtain their secret shares from bidders without a secure channel. Since it doesn't need a secure channel, this scheme is more practical and applicable to more scenarios. This scheme provides resistance to collusion attacks, conspiracy attacks, passive attacks and so on. Compared to [11, 12], our proposed scheme provides authentication without increasing the communication cost.","PeriodicalId":120179,"journal":{"name":"Proceedings of the 2015 Workshop on Privacy-Aware Mobile Computing","volume":"176 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120972798","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}
Zhongli Liu, Zupei Li, Benyuan Liu, Xinwen Fu, I. Raptis, K. Ren
Miniature (mini) drones are enjoying increasing attention. They have a broad market and applications. However, a powerful technology often has two ethical sides. Miniature drones can be abused, rendering security and privacy concerns. The contribution of this paper is two-fold. First, we will perform a survey of mini-drones on market and compare their specifications such as flight time, maximum payload weight, and price, and regulations and issues of operating mini-drones. Second, we propose novel aerial localization strategies and compare six different localization strategies for a thorough study of aerial localization by a single drone.
{"title":"Rise of Mini-Drones: Applications and Issues","authors":"Zhongli Liu, Zupei Li, Benyuan Liu, Xinwen Fu, I. Raptis, K. Ren","doi":"10.1145/2757302.2757303","DOIUrl":"https://doi.org/10.1145/2757302.2757303","url":null,"abstract":"Miniature (mini) drones are enjoying increasing attention. They have a broad market and applications. However, a powerful technology often has two ethical sides. Miniature drones can be abused, rendering security and privacy concerns. The contribution of this paper is two-fold. First, we will perform a survey of mini-drones on market and compare their specifications such as flight time, maximum payload weight, and price, and regulations and issues of operating mini-drones. Second, we propose novel aerial localization strategies and compare six different localization strategies for a thorough study of aerial localization by a single drone.","PeriodicalId":120179,"journal":{"name":"Proceedings of the 2015 Workshop on Privacy-Aware Mobile Computing","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123823564","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 recent years, wearable devices have seen an explosive growth of popularity and a rapid enhancement of functionalities. Current off-the-shelf wearable devices offer pack sensors such as pedometer, gyroscope, accelerometer, altimeter, compass, GPS, and heart rate monitor. These sensors work together to quietly monitor various aspects of a user's daily life, enabling a wide spectrum of health- and social-related applications. Nevertheless, the data collected by such sensors, even in their aggregated form, may cause significant privacy concerns if shared with third-party applications and/or a user's social connections (as many wearable platforms now support). This paper studies a novel problem of the potential inference of sensitive user behavior from seemingly insensitive sensor outputs. Specifically, we examine whether it is possible to infer the behavioral sequence of a user such as moving from one place to another, visiting a coffee shop, grocery shopping, etc., based on the outputs of pedometer sensors (aggregated over certain time intervals, e.g., 1 minute). We demonstrate through real-world experiments that it is often possible to infer such behavior with a high success probability, raising privacy concerns on the sharing of such information as currently supported by various wearable devices.
{"title":"Privacy Disclosure from Wearable Devices","authors":"Tong Yan, Yachao Lu, Nan Zhang","doi":"10.1145/2757302.2757306","DOIUrl":"https://doi.org/10.1145/2757302.2757306","url":null,"abstract":"In recent years, wearable devices have seen an explosive growth of popularity and a rapid enhancement of functionalities. Current off-the-shelf wearable devices offer pack sensors such as pedometer, gyroscope, accelerometer, altimeter, compass, GPS, and heart rate monitor. These sensors work together to quietly monitor various aspects of a user's daily life, enabling a wide spectrum of health- and social-related applications. Nevertheless, the data collected by such sensors, even in their aggregated form, may cause significant privacy concerns if shared with third-party applications and/or a user's social connections (as many wearable platforms now support). This paper studies a novel problem of the potential inference of sensitive user behavior from seemingly insensitive sensor outputs. Specifically, we examine whether it is possible to infer the behavioral sequence of a user such as moving from one place to another, visiting a coffee shop, grocery shopping, etc., based on the outputs of pedometer sensors (aggregated over certain time intervals, e.g., 1 minute). We demonstrate through real-world experiments that it is often possible to infer such behavior with a high success probability, raising privacy concerns on the sharing of such information as currently supported by various wearable devices.","PeriodicalId":120179,"journal":{"name":"Proceedings of the 2015 Workshop on Privacy-Aware Mobile Computing","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125216856","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}
It is our great pleasure to welcome you to the 2015 ACM MobiHoc Workshop on Privacy-Aware Mobile Computing -- PAMCO'15. This is the first year of this workshop, which aims to bring together researchers from mobile computing and security/privacy communities to discuss topics related to the protection of privacy in mobile computing, including both theoretical studies and implementation/experimentations papers, especially analysis of privacy threats from emerging applications in mobile environments - e.g., location-based services, mobile apps, wearable computing, etc.
{"title":"Proceedings of the 2015 Workshop on Privacy-Aware Mobile Computing","authors":"Xinwen Fu, Nan Zhang","doi":"10.1145/2757302","DOIUrl":"https://doi.org/10.1145/2757302","url":null,"abstract":"It is our great pleasure to welcome you to the 2015 ACM MobiHoc Workshop on Privacy-Aware Mobile Computing -- PAMCO'15. This is the first year of this workshop, which aims to bring together researchers from mobile computing and security/privacy communities to discuss topics related to the protection of privacy in mobile computing, including both theoretical studies and implementation/experimentations papers, especially analysis of privacy threats from emerging applications in mobile environments - e.g., location-based services, mobile apps, wearable computing, etc.","PeriodicalId":120179,"journal":{"name":"Proceedings of the 2015 Workshop on Privacy-Aware Mobile Computing","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114545814","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}
Qinggang Yue, Z. Ling, Wei Yu, Benyuan Liu, Xinwen Fu
In this paper, we investigate how to retrieve meaningful English text input on mobile devices from recorded videos while the text is illegible in the videos. In our previous work, we were able to retrieve random passwords with high success rate at a certain distance. When the distance increases, the success rate of recovering passwords decreases. However, if the input is meaningful text such as email messages, we can further increase the success rate via natural language processing techniques since the text follows spelling and grammar rules and is context sensitive. The process of retrieving the text from videos can be modeled as noisy channels. We first derive candidate words for each word of the input sentence, model the whole sentence with a Hidden Markov model and then apply the trigram language model to derive the original sentence. Our experiments validate our technique of retrieving meaningful English text input on mobile devices from recorded videos.
{"title":"Blind Recognition of Text Input on Mobile Devices via Natural Language Processing","authors":"Qinggang Yue, Z. Ling, Wei Yu, Benyuan Liu, Xinwen Fu","doi":"10.1145/2757302.2757304","DOIUrl":"https://doi.org/10.1145/2757302.2757304","url":null,"abstract":"In this paper, we investigate how to retrieve meaningful English text input on mobile devices from recorded videos while the text is illegible in the videos. In our previous work, we were able to retrieve random passwords with high success rate at a certain distance. When the distance increases, the success rate of recovering passwords decreases. However, if the input is meaningful text such as email messages, we can further increase the success rate via natural language processing techniques since the text follows spelling and grammar rules and is context sensitive. The process of retrieving the text from videos can be modeled as noisy channels. We first derive candidate words for each word of the input sentence, model the whole sentence with a Hidden Markov model and then apply the trigram language model to derive the original sentence. Our experiments validate our technique of retrieving meaningful English text input on mobile devices from recorded videos.","PeriodicalId":120179,"journal":{"name":"Proceedings of the 2015 Workshop on Privacy-Aware Mobile Computing","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126176213","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: Technical Paper Session","authors":"","doi":"10.1145/3246892","DOIUrl":"https://doi.org/10.1145/3246892","url":null,"abstract":"","PeriodicalId":120179,"journal":{"name":"Proceedings of the 2015 Workshop on Privacy-Aware Mobile Computing","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114119675","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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