Pub Date : 2021-11-20DOI: 10.2478/popets-2022-0019
Badih Ghazi, Ben Kreuter, Ravi Kumar, Pasin Manurangsi, Jiayu Peng, E. Skvortsov, Yao Wang, Craig Wright
Abstract Consider the setting where multiple parties each hold a multiset of users and the task is to estimate the reach (i.e., the number of distinct users appearing across all parties) and the frequency histogram (i.e., fraction of users appearing a given number of times across all parties). In this work we introduce a new sketch for this task, based on an exponentially distributed counting Bloom filter. We combine this sketch with a communication-efficient multi-party protocol to solve the task in the multi-worker setting. Our protocol exhibits both differential privacy and security guarantees in the honest-but-curious model and in the presence of large subsets of colluding workers; furthermore, its reach and frequency histogram estimates have a provably small error. Finally, we show the practicality of the protocol by evaluating it on internet-scale audiences.
{"title":"Multiparty Reach and Frequency Histogram: Private, Secure, and Practical","authors":"Badih Ghazi, Ben Kreuter, Ravi Kumar, Pasin Manurangsi, Jiayu Peng, E. Skvortsov, Yao Wang, Craig Wright","doi":"10.2478/popets-2022-0019","DOIUrl":"https://doi.org/10.2478/popets-2022-0019","url":null,"abstract":"Abstract Consider the setting where multiple parties each hold a multiset of users and the task is to estimate the reach (i.e., the number of distinct users appearing across all parties) and the frequency histogram (i.e., fraction of users appearing a given number of times across all parties). In this work we introduce a new sketch for this task, based on an exponentially distributed counting Bloom filter. We combine this sketch with a communication-efficient multi-party protocol to solve the task in the multi-worker setting. Our protocol exhibits both differential privacy and security guarantees in the honest-but-curious model and in the presence of large subsets of colluding workers; furthermore, its reach and frequency histogram estimates have a provably small error. Finally, we show the practicality of the protocol by evaluating it on internet-scale audiences.","PeriodicalId":74556,"journal":{"name":"Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47141021","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}
Pub Date : 2021-11-20DOI: 10.2478/popets-2022-0026
Lennart Oldenburg, Gunes Acar, C. Díaz
Abstract We present a novel web-based attack that identifies a Tor user’s guard in a matter of seconds. Our attack is low-cost, fast, and stealthy. It requires only a moderate amount of resources and can be deployed by website owners, third-party script providers, and malicious exits—if the website traffic is unencrypted. The attack works by injecting resources from non-existing onion service addresses into a webpage. Upon visiting the attack webpage with Tor Browser, the victim’s Tor client creates many circuits to look up the non-existing addresses. This allows middle relays controlled by the adversary to detect the distinctive traffic pattern of the “404 Not Found” lookups and identify the victim’s guard. We evaluate our attack with extensive simulations and live Tor network measurements, taking a range of victim machine, network, and geolocation configurations into account. We find that an adversary running a small number of HSDirs and providing 5 % of Tor’s relay bandwidth needs 12.06 seconds to identify the guards of 50 % of the victims, while it takes 22.01 seconds to discover 90 % of the victims’ guards. Finally, we evaluate a set of countermeasures against our attack including a defense that we develop based on a token bucket and the recently proposed Vanguards-lite defense in Tor.
我们提出了一种新的基于网络的攻击，可以在几秒钟内识别Tor用户的守卫。我们的攻击成本低，速度快，而且隐蔽。它只需要适量的资源，可以由网站所有者、第三方脚本提供商和恶意出口(如果网站流量未加密)部署。这种攻击通过将不存在的洋葱服务地址中的资源注入到网页中来实现。在使用Tor浏览器访问攻击网页时，受害者的Tor客户端创建了许多电路来查找不存在的地址。这允许由攻击者控制的中间中继检测“404 Not Found”查找的独特流量模式并识别受害者的守卫。我们通过广泛的模拟和实时Tor网络测量来评估我们的攻击，将一系列受害者机器，网络和地理位置配置考虑在内。我们发现，运行少量hsdir并提供Tor中继带宽5%的攻击者需要12.06秒才能识别50%受害者的守卫，而发现90%受害者的守卫需要22.01秒。最后，我们评估了一组针对我们攻击的对策，包括我们基于令牌桶开发的防御和最近在Tor中提出的先锋生命防御。
{"title":"From “Onion Not Found” to Guard Discovery","authors":"Lennart Oldenburg, Gunes Acar, C. Díaz","doi":"10.2478/popets-2022-0026","DOIUrl":"https://doi.org/10.2478/popets-2022-0026","url":null,"abstract":"Abstract We present a novel web-based attack that identifies a Tor user’s guard in a matter of seconds. Our attack is low-cost, fast, and stealthy. It requires only a moderate amount of resources and can be deployed by website owners, third-party script providers, and malicious exits—if the website traffic is unencrypted. The attack works by injecting resources from non-existing onion service addresses into a webpage. Upon visiting the attack webpage with Tor Browser, the victim’s Tor client creates many circuits to look up the non-existing addresses. This allows middle relays controlled by the adversary to detect the distinctive traffic pattern of the “404 Not Found” lookups and identify the victim’s guard. We evaluate our attack with extensive simulations and live Tor network measurements, taking a range of victim machine, network, and geolocation configurations into account. We find that an adversary running a small number of HSDirs and providing 5 % of Tor’s relay bandwidth needs 12.06 seconds to identify the guards of 50 % of the victims, while it takes 22.01 seconds to discover 90 % of the victims’ guards. Finally, we evaluate a set of countermeasures against our attack including a defense that we develop based on a token bucket and the recently proposed Vanguards-lite defense in Tor.","PeriodicalId":74556,"journal":{"name":"Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42957166","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}
Pub Date : 2021-11-20DOI: 10.2478/popets-2022-0030
Maggie Van Nortwick, Christo Wilson
Abstract On June 28, 2018, the California State Legislature passed the California Consumer Privacy Act (CCPA), arguably the most comprehensive piece of online privacy legislation in the United States. Online services covered by the CCPA are required to provide a hyperlink on their homepage with the text “Do Not Sell My Personal Information” (DNSMPI). The CCPA went into effect on January 1, 2020, a date that was chosen to give data collectors time to study the new law and bring themselves into compliance. In this study, we begin the process of investigating whether websites are complying with the CCPA by focusing on DNSMPI links. Using longitudinal data crawled from the top 1M websites in the Tranco ranking, we examine which websites are including DNSMPI links, whether the websites without DNSMPI links are out of compliance with the law, whether websites are using geofences to dynamically hide DNSMPI links from non-Californians, how DNSMPI adoption has changed over time, and how websites are choosing to present DNSMPI links (e.g., in terms of font size, color, and placement). We argue that the answers to these questions are critical for spurring enforcement actions under the law, and helping to shape future privacy laws and regulations, e.g., rule making that will soon commence around the successor to the CCPA, known as the CPRA.
{"title":"Setting the Bar Low: Are Websites Complying With the Minimum Requirements of the CCPA?","authors":"Maggie Van Nortwick, Christo Wilson","doi":"10.2478/popets-2022-0030","DOIUrl":"https://doi.org/10.2478/popets-2022-0030","url":null,"abstract":"Abstract On June 28, 2018, the California State Legislature passed the California Consumer Privacy Act (CCPA), arguably the most comprehensive piece of online privacy legislation in the United States. Online services covered by the CCPA are required to provide a hyperlink on their homepage with the text “Do Not Sell My Personal Information” (DNSMPI). The CCPA went into effect on January 1, 2020, a date that was chosen to give data collectors time to study the new law and bring themselves into compliance. In this study, we begin the process of investigating whether websites are complying with the CCPA by focusing on DNSMPI links. Using longitudinal data crawled from the top 1M websites in the Tranco ranking, we examine which websites are including DNSMPI links, whether the websites without DNSMPI links are out of compliance with the law, whether websites are using geofences to dynamically hide DNSMPI links from non-Californians, how DNSMPI adoption has changed over time, and how websites are choosing to present DNSMPI links (e.g., in terms of font size, color, and placement). We argue that the answers to these questions are critical for spurring enforcement actions under the law, and helping to shape future privacy laws and regulations, e.g., rule making that will soon commence around the successor to the CCPA, known as the CPRA.","PeriodicalId":74556,"journal":{"name":"Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45064154","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}
Pub Date : 2021-11-20DOI: 10.2478/popets-2022-0003
Jiafan Wang, Sherman S. M. Chow
Abstract Dynamic searchable symmetric encryption (DSSE) allows a client to query or update an outsourced encrypted database. Range queries are commonly needed. Previous range-searchable schemes either do not support updates natively (SIGMOD’16) or use file indexes of many long bit-vectors for distinct keywords, which only support toggling updates via homomorphically flipping the presence bit. (ESORICS’18). We propose a generic upgrade of any (inverted-index) DSSE to support range queries (a.k.a. range DSSE), without homomorphic encryption, and a specific instantiation with a new trade-off reducing client-side storage. Our schemes achieve forward security, an important property that mitigates file injection attacks. Moreover, we identify a variant of injection attacks against the first somewhat dynamic scheme (ESORICS’18). We also extend the definition of backward security to range DSSE and show that our schemes are compatible with a generic upgrade of backward security (CCS’17). We comprehensively analyze the computation and communication overheads, including implementation details of client-side index-related operations omitted by prior schemes. We show high empirical efficiency for million-scale databases over a million-scale keyword space.
{"title":"Forward and Backward-Secure Range-Searchable Symmetric Encryption","authors":"Jiafan Wang, Sherman S. M. Chow","doi":"10.2478/popets-2022-0003","DOIUrl":"https://doi.org/10.2478/popets-2022-0003","url":null,"abstract":"Abstract Dynamic searchable symmetric encryption (DSSE) allows a client to query or update an outsourced encrypted database. Range queries are commonly needed. Previous range-searchable schemes either do not support updates natively (SIGMOD’16) or use file indexes of many long bit-vectors for distinct keywords, which only support toggling updates via homomorphically flipping the presence bit. (ESORICS’18). We propose a generic upgrade of any (inverted-index) DSSE to support range queries (a.k.a. range DSSE), without homomorphic encryption, and a specific instantiation with a new trade-off reducing client-side storage. Our schemes achieve forward security, an important property that mitigates file injection attacks. Moreover, we identify a variant of injection attacks against the first somewhat dynamic scheme (ESORICS’18). We also extend the definition of backward security to range DSSE and show that our schemes are compatible with a generic upgrade of backward security (CCS’17). We comprehensively analyze the computation and communication overheads, including implementation details of client-side index-related operations omitted by prior schemes. We show high empirical efficiency for million-scale databases over a million-scale keyword space.","PeriodicalId":74556,"journal":{"name":"Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43613224","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}
Pub Date : 2021-11-20DOI: 10.2478/popets-2022-0006
M. Mehrnezhad, Kovila P. L. Coopamootoo, Ehsan Toreini
Abstract Online tracking is complex and users find it challenging to protect themselves from it. While the academic community has extensively studied systems and users for tracking practices, the link between the data protection regulations, websites’ practices of presenting privacy-enhancing technologies (PETs), and how users learn about PETs and practice them is not clear. This paper takes a multidimensional approach to find such a link. We conduct a study to evaluate the 100 top EU websites, where we find that information about PETs is provided far beyond the cookie notice. We also find that opting-out from privacy settings is not as easy as opting-in and becomes even more difficult (if not impossible) when the user decides to opt-out of previously accepted privacy settings. In addition, we conduct an online survey with 614 participants across three countries (UK, France, Germany) to gain a broad understanding of users’ tracking protection practices. We find that users mostly learn about PETs for tracking protection via their own research or with the help of family and friends. We find a disparity between what websites offer as tracking protection and the ways individuals report to do so. Observing such a disparity sheds light on why current policies and practices are ineffective in supporting the use of PETs by users.
{"title":"How Can and Would People Protect From Online Tracking?","authors":"M. Mehrnezhad, Kovila P. L. Coopamootoo, Ehsan Toreini","doi":"10.2478/popets-2022-0006","DOIUrl":"https://doi.org/10.2478/popets-2022-0006","url":null,"abstract":"Abstract Online tracking is complex and users find it challenging to protect themselves from it. While the academic community has extensively studied systems and users for tracking practices, the link between the data protection regulations, websites’ practices of presenting privacy-enhancing technologies (PETs), and how users learn about PETs and practice them is not clear. This paper takes a multidimensional approach to find such a link. We conduct a study to evaluate the 100 top EU websites, where we find that information about PETs is provided far beyond the cookie notice. We also find that opting-out from privacy settings is not as easy as opting-in and becomes even more difficult (if not impossible) when the user decides to opt-out of previously accepted privacy settings. In addition, we conduct an online survey with 614 participants across three countries (UK, France, Germany) to gain a broad understanding of users’ tracking protection practices. We find that users mostly learn about PETs for tracking protection via their own research or with the help of family and friends. We find a disparity between what websites offer as tracking protection and the ways individuals report to do so. Observing such a disparity sheds light on why current policies and practices are ineffective in supporting the use of PETs by users.","PeriodicalId":74556,"journal":{"name":"Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48410923","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}
Pub Date : 2021-11-20DOI: 10.2478/popets-2022-0018
Nishanth Chandran, Divya Gupta, Akash Shah
Abstract In 2-party Circuit-based Private Set Intersection (Circuit-PSI), P0 and P1 hold sets S0 and S1 respectively and wish to securely compute a function f over the set S0 ∩ S1 (e.g., cardinality, sum over associated attributes, or threshold intersection). Following a long line of work, Pinkas et al. (PSTY, Eurocrypt 2019) showed how to construct a concretely efficient Circuit-PSI protocol with linear communication complexity. However, their protocol requires super-linear computation. In this work, we construct concretely efficient Circuit-PSI protocols with linear computational and communication cost. Further, our protocols are more performant than the state-of-the-art, PSTY – we are ≈ 2.3× more communication efficient and are up to 2.8× faster. We obtain our improvements through a new primitive called Relaxed Batch Oblivious Programmable Pseudorandom Functions (RB-OPPRF) that can be seen as a strict generalization of Batch OPPRFs that were used in PSTY. This primitive could be of independent interest.
{"title":"Circuit-PSI With Linear Complexity via Relaxed Batch OPPRF","authors":"Nishanth Chandran, Divya Gupta, Akash Shah","doi":"10.2478/popets-2022-0018","DOIUrl":"https://doi.org/10.2478/popets-2022-0018","url":null,"abstract":"Abstract In 2-party Circuit-based Private Set Intersection (Circuit-PSI), P0 and P1 hold sets S0 and S1 respectively and wish to securely compute a function f over the set S0 ∩ S1 (e.g., cardinality, sum over associated attributes, or threshold intersection). Following a long line of work, Pinkas et al. (PSTY, Eurocrypt 2019) showed how to construct a concretely efficient Circuit-PSI protocol with linear communication complexity. However, their protocol requires super-linear computation. In this work, we construct concretely efficient Circuit-PSI protocols with linear computational and communication cost. Further, our protocols are more performant than the state-of-the-art, PSTY – we are ≈ 2.3× more communication efficient and are up to 2.8× faster. We obtain our improvements through a new primitive called Relaxed Batch Oblivious Programmable Pseudorandom Functions (RB-OPPRF) that can be seen as a strict generalization of Batch OPPRFs that were used in PSTY. This primitive could be of independent interest.","PeriodicalId":74556,"journal":{"name":"Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47132766","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}
Pub Date : 2021-11-20DOI: 10.2478/popets-2022-0031
M. Namara, Henry Sloan, Bart P. Knijnenburg
Abstract Research finds that the users of Social Networking Sites (SNSs) often fail to comprehensively engage with the plethora of available privacy features— arguably due to their sheer number and the fact that they are often hidden from sight. As different users are likely interested in engaging with different subsets of privacy features, an SNS could improve privacy management practices by adapting its interface in a way that proactively assists, guides, or prompts users to engage with the subset of privacy features they are most likely to benefit from. Whereas recent work presents algorithmic implementations of such privacy adaptation methods, this study investigates the optimal user interface mechanism to present such adaptations. In particular, we tested three proposed “adaptation methods” (automation, suggestions, highlights) in an online between-subjects user experiment in which 406 participants used a carefully controlled SNS prototype. We systematically evaluate the effect of these adaptation methods on participants’ engagement with the privacy features, their tendency to set stricter settings (protection), and their subjective evaluation of the assigned adaptation method. We find that the automation of privacy features afforded users the most privacy protection, while giving privacy suggestions caused the highest level of engagement with the features and the highest subjective ratings (as long as awkward suggestions are avoided). We discuss the practical implications of these findings in the effectiveness of adaptations improving user awareness of, and engagement with, privacy features on social media.
{"title":"The Effectiveness of Adaptation Methods in Improving User Engagement and Privacy Protection on Social Network Sites","authors":"M. Namara, Henry Sloan, Bart P. Knijnenburg","doi":"10.2478/popets-2022-0031","DOIUrl":"https://doi.org/10.2478/popets-2022-0031","url":null,"abstract":"Abstract Research finds that the users of Social Networking Sites (SNSs) often fail to comprehensively engage with the plethora of available privacy features— arguably due to their sheer number and the fact that they are often hidden from sight. As different users are likely interested in engaging with different subsets of privacy features, an SNS could improve privacy management practices by adapting its interface in a way that proactively assists, guides, or prompts users to engage with the subset of privacy features they are most likely to benefit from. Whereas recent work presents algorithmic implementations of such privacy adaptation methods, this study investigates the optimal user interface mechanism to present such adaptations. In particular, we tested three proposed “adaptation methods” (automation, suggestions, highlights) in an online between-subjects user experiment in which 406 participants used a carefully controlled SNS prototype. We systematically evaluate the effect of these adaptation methods on participants’ engagement with the privacy features, their tendency to set stricter settings (protection), and their subjective evaluation of the assigned adaptation method. We find that the automation of privacy features afforded users the most privacy protection, while giving privacy suggestions caused the highest level of engagement with the features and the highest subjective ratings (as long as awkward suggestions are avoided). We discuss the practical implications of these findings in the effectiveness of adaptations improving user awareness of, and engagement with, privacy features on social media.","PeriodicalId":74556,"journal":{"name":"Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47242930","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}
Pub Date : 2021-11-20DOI: 10.2478/popets-2022-0021
S. Avizheh, Preston Haffey, R. Safavi-Naini
Abstract Fair exchange protocols are among the most important cryptographic primitives in electronic commerce. A basic fair exchange protocol requires that two parties who want to exchange their digital items either receive what they have been promised, or lose nothing. Privacy of fair exchange requires that no one else (other than the two parties) learns anything about the items. Fairness and privacy have been considered as two distinct properties of an exchange protocol. In this paper, we show that subtle ways of leaking the exchange item to the third parties affect fairness in fair exchange protocols when the item is confidential. Our focus is on Fair-Swap, a recently proposed fair exchange protocol that uses a smart contract for dispute resolution, has proven security in UC (Universal Composability) framework, and provides privacy when both parties are honest. We demonstrate, however, that FairSwap’s dispute resolution protocol leaks information to the public and this leakage provides opportunities for the dishonest parties to influence the protocol’s fairness guarantee. We then propose an efficient privacy-enhanced version of Fair-Swap, prove its security and give an implementation and performance evaluation of our proposed system. Our privacy enhancement uses circuit randomization, and we prove its security and privacy in an extension of universal composability model for non-monolithic adversaries that would be of independent interest.
{"title":"Privacy-preserving FairSwap: Fairness and privacy interplay","authors":"S. Avizheh, Preston Haffey, R. Safavi-Naini","doi":"10.2478/popets-2022-0021","DOIUrl":"https://doi.org/10.2478/popets-2022-0021","url":null,"abstract":"Abstract Fair exchange protocols are among the most important cryptographic primitives in electronic commerce. A basic fair exchange protocol requires that two parties who want to exchange their digital items either receive what they have been promised, or lose nothing. Privacy of fair exchange requires that no one else (other than the two parties) learns anything about the items. Fairness and privacy have been considered as two distinct properties of an exchange protocol. In this paper, we show that subtle ways of leaking the exchange item to the third parties affect fairness in fair exchange protocols when the item is confidential. Our focus is on Fair-Swap, a recently proposed fair exchange protocol that uses a smart contract for dispute resolution, has proven security in UC (Universal Composability) framework, and provides privacy when both parties are honest. We demonstrate, however, that FairSwap’s dispute resolution protocol leaks information to the public and this leakage provides opportunities for the dishonest parties to influence the protocol’s fairness guarantee. We then propose an efficient privacy-enhanced version of Fair-Swap, prove its security and give an implementation and performance evaluation of our proposed system. Our privacy enhancement uses circuit randomization, and we prove its security and privacy in an extension of universal composability model for non-monolithic adversaries that would be of independent interest.","PeriodicalId":74556,"journal":{"name":"Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44074670","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}
Pub Date : 2021-11-20DOI: 10.2478/popets-2022-0025
Konstantinos Athanasiou, T. Wahl, A. Ding, Yunsi Fei
Abstract Recent advances in machine learning have enabled Neural Network (NN) inference directly on constrained embedded devices. This local approach enhances the privacy of user data, as the inputs to the NN inference are not shared with third-party cloud providers over a communication network. At the same time, however, performing local NN inference on embedded devices opens up the possibility of Power Analysis attacks, which have recently been shown to be effective in recovering NN parameters, as well as their activations and structure. Knowledge of these NN characteristics constitutes a privacy threat, as it enables highly effective Membership Inference and Model Inversion attacks, which can recover information about the sensitive data that the NN model was trained on. In this paper we address the problem of securing sensitive NN inference parameters against Power Analysis attacks. Our approach employs masking, a countermeasure well-studied in the context of cryptographic algorithms. We design a set of gadgets, i.e., masked operations, tailored to NN inference. We prove our proposed gadgets secure against power attacks and show, both formally and experimentally, that they are composable, resulting in secure NN inference. We further propose optimizations that exploit intrinsic characteristics of NN inference to reduce the masking’s runtime and randomness requirements. We empirically evaluate the performance of our constructions, showing them to incur a slowdown by a factor of about 2–5.
{"title":"Masking Feedforward Neural Networks Against Power Analysis Attacks","authors":"Konstantinos Athanasiou, T. Wahl, A. Ding, Yunsi Fei","doi":"10.2478/popets-2022-0025","DOIUrl":"https://doi.org/10.2478/popets-2022-0025","url":null,"abstract":"Abstract Recent advances in machine learning have enabled Neural Network (NN) inference directly on constrained embedded devices. This local approach enhances the privacy of user data, as the inputs to the NN inference are not shared with third-party cloud providers over a communication network. At the same time, however, performing local NN inference on embedded devices opens up the possibility of Power Analysis attacks, which have recently been shown to be effective in recovering NN parameters, as well as their activations and structure. Knowledge of these NN characteristics constitutes a privacy threat, as it enables highly effective Membership Inference and Model Inversion attacks, which can recover information about the sensitive data that the NN model was trained on. In this paper we address the problem of securing sensitive NN inference parameters against Power Analysis attacks. Our approach employs masking, a countermeasure well-studied in the context of cryptographic algorithms. We design a set of gadgets, i.e., masked operations, tailored to NN inference. We prove our proposed gadgets secure against power attacks and show, both formally and experimentally, that they are composable, resulting in secure NN inference. We further propose optimizations that exploit intrinsic characteristics of NN inference to reduce the masking’s runtime and randomness requirements. We empirically evaluate the performance of our constructions, showing them to incur a slowdown by a factor of about 2–5.","PeriodicalId":74556,"journal":{"name":"Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69252427","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}
Pub Date : 2021-11-20DOI: 10.2478/popets-2022-0028
Vinh-Thong Ta, M. Eiza
Abstract Privacy and data protection by design are relevant parts of the General Data Protection Regulation (GDPR), in which businesses and organisations are encouraged to implement measures at an early stage of the system design phase to fulfil data protection requirements. This paper addresses the policy and system architecture design and propose two variants of privacy policy language and architecture description language, respectively, for specifying and verifying data protection and privacy requirements. In addition, we develop a fully automated algorithm based on logic, for verifying three types of conformance relations (privacy, data protection, and functional conformance) between a policy and an architecture specified in our languages’ variants. Compared to related works, this approach supports a more systematic and fine-grained analysis of the privacy, data protection, and functional properties of a system. Our theoretical methods are then implemented as a software tool called DataProVe and its feasibility is demonstrated based on the centralised and decentralised approaches of COVID-19 contact tracing applications.
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