Content distribution systems have traditionally adopted one of two architectures: infrastructure-based content delivery networks (CDNs), in which clients download content from dedicated, centrally managed servers, and peer-topeer CDNs, in which clients download content from each other. The advantages and disadvantages of each architecture have been studied in great detail. Recently, hybrid, or "peer-assisted", CDNs have emerged, which combine elements from both architectures. The properties of such systems, however, are not as well understood.In this paper, we discuss the potential risks and benefits of peer-assisted CDNs, and we study one specific instance, Akamai's NetSession system, to examine the impact of these risks and benefits in practice. NetSession is a mature system that has been operating commercially since 2010 and currently has more than 25 million users in 239 countries and territories. Our results show that NetSession can deliver several of the key benefits of both infrastructure-based and peer-to-peer CDNs-for instance, it can offload 70-80% of the traffic to the peers without a corresponding loss of performance or reliabilityand that the risks can be managed well.This suggests that hybrid designs may be an attractive option for future CDNs.

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The ubiquity of portable mobile devices equipped with built-in cameras have led to a transformation in how and when digital images are captured, shared, and archived. Photographs and videos from social gatherings, public events, and even crime scenes are commonplace online. While the spontaneity afforded by these devices have led to new personal and creative outlets, privacy concerns of bystanders (and indeed, in some cases, unwilling subjects) have remained largely unaddressed. We present I-Pic, a trusted software platform that integrates digital capture with user-defined privacy. In I-Pic, users choose a level of privacy (e.g., image capture allowed or not) based upon social context (e.g., out in public vs. with friends vs. at workplace). Privacy choices of nearby users are advertised via short-range radio, and I-Pic-compliant capture platforms generate edited media to conform to privacy choices of image subjects. I-Pic uses secure multiparty computation to ensure that users' visual features and privacy choices are not revealed publicly, regardless of whether they are the subjects of an image capture. Just as importantly, I-Pic preserves the ease-of-use and spontaneous nature of capture and sharing between trusted users. Our evaluation of I-Pic shows that a practical, energy-efficient system that conforms to the privacy choices of many users within a scene can be built and deployed using current hardware.

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With the advancement of machine learning (ML) and its growing awareness, many organizations who own data but not ML expertise (data owner) would like to pool their data and collaborate with those who have expertise but need data from diverse sources to train truly generalizable models (model owner). In such collaborative ML, the data owner wants to protect the privacy of its training data, while the model owner desires the confidentiality of the model and the training method which may contain intellectual properties. However, existing private ML solutions, such as federated learning and split learning, cannot meet the privacy requirements of both data and model owners at the same time.This paper presents Citadel, a scalable collaborative ML system that protects the privacy of both data owner and model owner in untrusted infrastructures with the help of Intel SGX. Citadel performs distributed training across multiple training enclaves running on behalf of data owners and an aggregator enclave on behalf of the model owner. Citadel further establishes a strong information barrier between these enclaves by means of zero-sum masking and hierarchical aggregation to prevent data/model leakage during collaborative training. Compared with the existing SGX-protected training systems, Citadel enables better scalability and stronger privacy guarantees for collaborative ML. Cloud deployment with various ML models shows that Citadel scales to a large number of enclaves with less than 1.73X slowdown caused by SGX.