Brotli

Alakuijala, Jyrki; Farruggia, Andrea; Ferragina, Paolo; Kliuchnikov, Eugene; Obryk, Robert; Szabadka, Zoltan; Vandevenne, Lode

doi:10.1145/3231935

Cited by 53 publications

(23 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Google Photos does not publicly specify its processing pipeline, but we observe it to downsample the JPEG chroma format to 4:2:0, and to apply compression with possibly a noise filter. The compression is likely a standard JPEG quantization plus Google's own Guetzli JPEG encoder [4,5]. ESP encryption must account for these techniques or images will be corrupted.…”

Section: Format-preserving Encrypted Imagesmentioning

confidence: 99%

“…ESP converts an image to RGB, encrypts it in RGB color space using a block-based Fisher-Yates shuffle [19] with a per-image encryption key, splits the RGB channels into three separate grayscale ciphertext images, then converts them back into the original image format, including compression for JPEG. This novel encrypted grayscale approach maintains the original image dimensions and ensures compatibility with standard JPEG compression, widely-used compression techniques such as Guetzli [4,5] JPEG encoding, and JPEG chroma subsampling.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Encrypted cloud photo storage using Google photos

Koh

Nieh

Bellovin

2021

Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services

View full text Add to dashboard Cite

Cloud photo services are widely used for persistent, convenient, and often free photo storage, which is especially useful for mobile devices. As users store more and more photos in the cloud, significant privacy concerns arise because even a single compromise of a user's credentials give attackers unfettered access to all of the user's photos. We have created Easy Secure Photos (ESP) to enable users to protect their photos on cloud photo services such as Google Photos. ESP introduces a new client-side encryption architecture that includes a novel format-preserving image encryption algorithm, an encrypted thumbnail display mechanism, and a usable key management system. ESP encrypts image data such that the result is still a standard format image like JPEG that is compatible with cloud photo services. ESP efficiently generates and displays encrypted thumbnails for fast and easy browsing of photo galleries from trusted user devices. ESP's key management makes it simple to authorize multiple user devices to view encrypted image content via a process similar to device pairing, but using the cloud photo service as a QR code communication channel. We have implemented ESP in a popular Android photos app for use with Google Photos and demonstrate that it is easy to use and provides encryption functionality transparently to users, maintains good interactive performance and image quality while providing strong privacy guarantees, and retains the sharing and storage benefits of Google Photos without any changes to the cloud service. CCS CONCEPTS• Security and privacy → Key management; Public key encryption; Mobile and wireless security; Usability in security and privacy; Privacy protections; Software security engineering; Distributed systems security; • Computer systems organization → Cloud computing; • Human-centered computing → Ubiquitous and mobile devices; • Information systems → Web services.

show abstract

Section: Format-preserving Encrypted Imagesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Encrypted cloud photo storage using Google photos

Koh

Nieh

Bellovin

2021

Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services

View full text Add to dashboard Cite

show abstract

“…There are compression algorithms optimized for time-series data that are not specific to the automotive domain [45,46]. Furthermore, there are generalized compression algorithms that can handle arbitrary binary data [47][48][49][50][51][52][53]. We have created a domain-specific and lossless data preprocessing strategy and combined it with an existing generalized compression algorithm.…”

Section: Academiamentioning

confidence: 99%

Demand-Driven Data Acquisition for Large Scale Fleets

Matesanz

Graen

Fiege

et al. 2021

Sensors

View full text Add to dashboard Cite

Automakers manage vast fleets of connected vehicles and face an ever-increasing demand for their sensor readings. This demand originates from many stakeholders, each potentially requiring different sensors from different vehicles. Currently, this demand remains largely unfulfilled due to a lack of systems that can handle such diverse demands efficiently. Vehicles are usually passive participants in data acquisition, each continuously reading and transmitting the same static set of sensors. However, in a multi-tenant setup with diverse data demands, each vehicle potentially needs to provide different data instead. We present a system that performs such vehicle-specific minimization of data acquisition by mapping individual data demands to individual vehicles. We collect personal data only after prior consent and fulfill the requirements of the GDPR. Non-personal data can be collected by directly addressing individual vehicles. The system consists of a software component natively integrated with a major automaker’s vehicle platform and a cloud platform brokering access to acquired data. Sensor readings are either provided via near real-time streaming or as recorded trip files that provide specific consistency guarantees. A performance evaluation with over 200,000 simulated vehicles has shown that our system can increase server capacity on-demand and process streaming data within 269 ms on average during peak load. The resulting architecture can be used by other automakers or operators of large sensor networks. Native vehicle integration is not mandatory; the architecture can also be used with retrofitted hardware such as OBD readers.

show abstract

“…LZFSE is an Apple compressor with finite-state entropy, which combines a dictionary compression scheme with a technique based on asymmetric numeral systems [137]. Brotli is the Google approach to replace Zopfli and is not DEFLATE-compatible [8]. The compressed file is represented by a collection of meta-blocks.…”

Section: Lz77mentioning

confidence: 99%

State of the Art and Future Trends in Data Reduction for High-Performance Computing

2020

JSFI

View full text Add to dashboard Cite

Research into data reduction techniques has gained popularity in recent years as storage capacity and performance become a growing concern. This survey paper provides an overview of leveraging points found in high-performance computing (HPC) systems and suitable mechanisms to reduce data volumes. We present the underlying theories and their application throughout the HPC stack and also discuss related hardware acceleration and reduction approaches. After introducing relevant use-cases, an overview of modern lossless and lossy compression algorithms and their respective usage at the application and file system layer is given. In anticipation of their increasing relevance for adaptive and in situ approaches, dimensionality reduction techniques are summarized with a focus on non-linear feature extraction. Adaptive approaches and in situ compression algorithms and frameworks follow. The key stages and new opportunities to deduplication are covered next. An unconventional but promising method is recomputation, which is proposed at last. We conclude the survey with an outlook on future developments.

show abstract

Brotli

Cited by 53 publications

References 16 publications

Encrypted cloud photo storage using Google photos

Encrypted cloud photo storage using Google photos

Demand-Driven Data Acquisition for Large Scale Fleets

State of the Art and Future Trends in Data Reduction for High-Performance Computing

Contact Info

Product

Resources

About