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1. A Verified Confidential Computing as a Service Framework for Privacy Preservation

   Chen, Hongbo; Chen, Haobin; Sun, Mingshen; Li, Kang; Chen, Zhaofeng; Wang, XiaoFeng (August 2023, the Proceedings of the 32nd USENIX Security Symposium)

   Joe Calandrino and Carmela Troncoso (Ed.)

   As service providers are moving to the cloud, users are forced to provision sensitive data to the cloud. Confidential computing leverages hardware Trusted Execution Environment (TEE) to protect data in use, no longer requiring users’ trust to the cloud. The emerging service model, Confidential Computing as a Service (CCaaS), is adopted by service providers to offer service similar to the Function-as-a-Serivce manner. However, privacy concerns are raised in CCaaS, especially in multi-user scenarios. CCaaS need to assure the data providers that the service does not leak their privacy to any unauthorized parties and clear their data after the service. To address such privacy concerns with security guarantees, we first formally define the security objective, Proof of Being Forgotten (PoBF), and prove under which security constraints PoBF can be satisfied. Then, these constraints serve as guidelines in the implementation of the PoBF-compliant Framework (PoCF). PoCF consists of a generic library for different hardware TEEs, CCaaS prototype enclaves, and a verifier to prove PoBF-compliance. PoCF leverages Rust’s robust type system and security features, to construct a verified state machine with privacy-preserving contracts. Last, the experiment results show that the protections introduced by PoCF incur minor runtime performance overhead. 

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2. The Nuanced Nature of Trust and Privacy Control Adoption in the Context of Google

   https://doi.org/10.1145/3544548.3581387  

   Ul Haque, Ehsan; Khan, Mohammad Maifi; Fahim, Md Abdullah (April 2023, CHI Conference on Human Factors in Computing Systems (CHI ‘23))

   This paper investigates how trust towards service providers and the adoption of privacy controls belonging to two specific purposes (control over “sharing” vs. “usage” of data) vary based on users’ technical literacy. Towards that, we chose Google as the context and conducted an online survey across 209 Google users. Our results suggest that integrity and benevolence perceptions toward Google are significantly lower among technical participants than non-technical participants. While trust perceptions differ between non-technical adopters and non-adopters of privacy controls, no such difference is found among the technical counterparts. Notably, among the non-technical participants, the direction of trust affecting privacy control adoption is observed to be reversed based on the purpose of the controls. Using qualitative analysis, we extract trust-enhancing and dampening factors contributing to users’ trusting beliefs towards Google’s protection of user privacy. The implications of our findings for the design and promotion of privacy controls are discussed in the paper. 

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3. Toward User Control over Information Access: A Sociotechnical Approach

   https://doi.org/10.1145/3584318.3584327  

   Malchik, Caleb; Feigenbaum, Joan (October 2022, Proceedings of the 2022 New Security Paradigms Workshop)

   We study the relationship between Web users and service providers, taking a sociotechnical approach and focusing particularly (but not exclusively) on privacy and security of personal data. Much conventional Web-security practice seeks to protect benevolent parties, both individuals and organizations, against purely malev- olent adversaries in an effort to prevent catastrophic events such as data breaches, ransomware attacks, and denial of service. By contrast, we highlight the dynamics among the parties that much conventional security technology seeks to protect. We regard most interactions between users and providers as implicit negotiations that, like the interactions between buyers and sellers in a market- place, have both adversarial and cooperative aspects. Our goal is to rebalance these negotiations in order to give more power to users; toward that end we advocate the adoption of two techniques, one technical and one organizational. Technically, we introduce the Plat- form for Untrusted Resource Evaluation (PURE), a content-labeling framework that empowers users to make informed decisions about service providers, reduces the ability of providers to induce be- haviors that benefit them more than users, and requires minimal time and effort to use. On the organizational side, we concur with Gordon-Tapiero et al. [19] that a collective approach is necessary to rebalance the power dynamics between users and providers; in par- ticular, we suggest that the data co-op, an organizational form sug- gested by Ligett and Nissim [25] and Pentland and Hardjono [28], is a natural setting in which to deploy PURE and similar tools. 

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4. Delphi: A Cryptographic Inference System for Neural Networks

   https://doi.org/10.1145/3411501.3419418  

   Mishra, Pratyush; Lehmkuhl, Ryan; Srinivasan, Akshayaram; Zheng, Wenting; Popa, Raluca Ada (November 2020, PPMLP'20: Proceedings of the 2020 Workshop on Privacy-Preserving Machine Learning in Practice)

   null (Ed.)

   Many companies provide neural network prediction services to users for a wide range of applications. However, current prediction systems compromise one party's privacy: either the user has to send sensitive inputs to the service provider for classification, or the service provider must store its proprietary neural networks on the user's device. The former harms the personal privacy of the user, while the latter reveals the service provider's proprietary model. We design, implement, and evaluate Delphi, a secure prediction system that allows two parties to execute neural network inference without revealing either party's data. Delphi approaches the problem by simultaneously co-designing cryptography and machine learning. We first design a hybrid cryptographic protocol that improves upon the communication and computation costs over prior work. Second, we develop a planner that automatically generates neural network architecture configurations that navigate the performance-accuracy trade-offs of our hybrid protocol. Together, these techniques allow us to achieve a 22x improvement in online prediction latency compared to the state-of-the-art prior work. 

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5. A privacy-preserving networked hospitality service with the bitcoin blockchain

   https://doi.org/10.1007/978-3-319-94268-1_57  

   Zhou, H. (June 2018, The 13th International Conference on Wireless Algorithms, Systems, and Applications (WASA))

   In recent years, we have witnessed a rise in the popularity of net- worked hospitality services (NHSs), an online marketplace for short-term peer- to-peer accommodations. Such systems, however, raise significant privacy con- cerns, because service providers such as Airbnb and 9flats can easily collect the precise and personal information of millions of participating hosts and guests through their centralized online platforms. In this paper, we propose PrivateNH, a privacy-enhancing and practical solution that offers anonymity and accountabil- ity for NHS users without relying on any trusted third party. PrivateNH leverages the recent progress of Bitcoin techniques such as Colored Coins and CoinShuffle to generate and maintain anonymous credentials for NHS participants. The cre- dential holders (NHS hosts or guests) can then lease or rent short-term lodging and interact with the service provider in an anonymous and accountable man- ner. An anonymous and secure reputation system is also introduced to establish the trust between unfamiliar hosts and guests in a peer-to-peer fashion. The pro- posed scheme is compatible with the current Bitcoin blockchain system, and its effectiveness and feasibility in NHS scenario are also demonstrated by security analysis and performance evaluation. 

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