Sphinx: A Compact and Provably Secure Mix Format

By George Danezis, Ian Goldberg. IEEE Symposium on Security and Privacy, 2009

301 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/418df46d2dfff04c0adda1e0ded3455a523218a8

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Citations in the corpus

Montreal Forced Aligner: Trainable Text-Speech Alignment Using Kaldi (2017, background): “1We do not address related work, such as on linguistic analysis of untranscribed speech [9], or phoneme boundary detection [10], or textspeech alignment for TTS [11].”
Deep Learning on FPGAs: Past, Present, and Future (2016, background): “This project has claimed to improve large-scale search engine performance by a factor of two, showing promise for this type of FPGA application [4].”
Concurrency and Privacy with Payment-Channel Networks (2017, methodology): “In our implementation, however, we use Sphinx [31] to create a single anonymous communication channel between sender and receiver, where intermediate nodes are the intermediate users in the path.”
Black-box $\alpha$-divergence Minimization (2015, background): “But unlike EP, which computes different cavity distributions for different data points, BB-α uses the same cavity distribution for each data point.”
Expectation propagation for neural networks with sparsity-promoting priors (2013, background): “…j , σ̃2φ, j}Kdj=1 of the prior site approximations (14) associated with the input weightsw and the scale parametersφ (Section 3.2.1). end Loop over the non-Gaussian likelihood terms: for i← 1 to n do 2 Compute the means and covariances of the cavity distributions: {q−i(hi,k)}Kk=1 and q−i(v) using…”
Stochastic Expectation Propagation (2015, methodology): “In contrast, variational inference (VI) methods [14, 15] utilise global approximations that are reﬁned directly, which prevents memory overheads from scaling with N .”
A Protocol for Privately Reporting Ad Impressions at Scale (2016, background): “These run the range from improvements to Tor [41], to alternatives [31, 33, 35], to a line of works on scalable mixnets [20, 21], and dining cryptographer networks [17, 43, 18].”
Double Thompson Sampling for Dueling Bandits (2016, methodology): “Here we present the results for experiments based on the Microsoft Learning to Rank (MSLR) dataset [23], which provides the relevance for queries and ranked documents.”
Bayesian Inference and Optimal Design in the Sparse Linear Model (2007, background): “A way to circumvent this problem is to employ fractional EP updates (these have been introduced in a different context in [7]), which can be understood by imagining the site t i being replaced by q fractional replicas f i = t 1/q i .”