Separating Local & Shuffled Differential Privacy via Histograms

Recent work in differential privacy has highlighted the shuffled model as a promising avenue to compute accurate statistics while keeping raw data in users\u27 hands. We present a protocol in this model that estimates histograms with error independent of the domain size. This implies an arbitrarily large gap in sample complexity between the shuffled and local models. On the other hand, we show that the models are equivalent when we impose the constraints of pure differential privacy and single-message randomizers

Differential Privacy on Finite Computers

We consider the problem of designing and analyzing differentially private algorithms that can be implemented on discrete models of computation in strict polynomial time, motivated by known attacks on floating point implementations of real-arithmetic differentially private algorithms (Mironov, CCS 2012) and the potential for timing attacks on expected polynomial-time algorithms. We use a case study: the basic problem of approximating the histogram of a categorical dataset over a possibly large data universe X. The classic Laplace Mechanism (Dwork, McSherry, Nissim, Smith, TCC 2006 and J. Privacy & Confidentiality 2017) does not satisfy our requirements, as it is based on real arithmetic, and natural discrete analogues, such as the Geometric Mechanism (Ghosh, Roughgarden, Sundarajan, STOC 2009 and SICOMP 2012), take time at least linear in |X|, which can be exponential in the bit length of the input. In this paper, we provide strict polynomial-time discrete algorithms for approximate histograms whose simultaneous accuracy (the maximum error over all bins) matches that of the Laplace Mechanism up to constant factors, while retaining the same (pure) differential privacy guarantee. One of our algorithms produces a sparse histogram as output. Its "per-bin accuracy" (the error on individual bins) is worse than that of the Laplace Mechanism by a factor of log |X|, but we prove a lower bound showing that this is necessary for any algorithm that produces a sparse histogram. A second algorithm avoids this lower bound, and matches the per-bin accuracy of the Laplace Mechanism, by producing a compact and efficiently computable representation of a dense histogram; it is based on an (n+1)-wise independent implementation of an appropriately clamped version of the Discrete Geometric Mechanism

Novel Benchmark Values for Open Major Anatomic Liver Resection in Non-Cirrhotic Patients. A Multicentric Study of 44 International Expert Centers

Objective: This study aims at establishing benchmark values for best achievable outcomes following open major anatomic hepatectomy for liver tumors of all dignities. Background: Outcomes after open major hepatectomies vary widely lacking reference values for comparisons among centers, indications, types of resections, and minimally invasive procedures. Methods: A standard benchmark methodology was used covering consecutive patients, who underwent open major anatomic hepatectomy from 44 high-volume liver centers from 5 continents over a five-year period (2016–2020). Benchmark cases were low-risk non-cirrhotic patients without significant co-morbidities treated in high-volume centers (≥30 major liver resections/year). Benchmark values were set at the 75th percentile of median values of all centers. Minimum follow-up period was 1 year in each patient. Results: Of 8044 patients, 2908 (36%) qualified as benchmark (low risk) cases. Benchmark cutoffs for all indications include R0 resection ≥78%; liver failure (grade B/C) ≤10%; bile leak (grade B/C) ≤18%; complications ≥grade 3 and CCI® ≤46% and ≤9 at 3 months, respectively. Benchmark values differed significantly between malignant and benign conditions so that reference values must be adjusted accordingly. Extended right hepatectomy (H1,4-8 or H4-8) disclosed higher cutoff for liver failure, while extended left (H1-5,8 or H2-5,8) were associated with higher cutoffs for bile leaks, but had superior oncologic outcomes, when compared to formal left hepatectomy (H1-4 or H2-4). The minimal follow up for a conclusive outcome evaluation following open anatomic major resection must be 3 months. Conclusion: These new benchmark cut-offs for open major hepatectomy provide a powerful tool to convincingly evaluate other approaches including parenchymal-sparing procedures, laparoscopic/robotic approaches, and alternative treatments, such as ablation therapy, irradiation or novel chemotherapy regimens

Novel Benchmark Values for Open Major Anatomic Liver Resection in Non-Cirrhotic Patients. A Multicentric Study of 44 International Expert Centers

OBJECTIVE: This study aims at establishing benchmark values for best achievable outcomes following open major anatomic hepatectomy for liver tumors of all dignities. BACKGROUND: Outcomes after open major hepatectomies vary widely lacking reference values for comparisons among centers, indications, types of resections, and minimally invasive procedures. METHODS: A standard benchmark methodology was used covering consecutive patients, who underwent open major anatomic hepatectomy from 44 high-volume liver centers from 5 continents over a 5-year period (2016-2020). Benchmark cases were low-risk non-cirrhotic patients without significant comorbidities treated in high-volume centers (≥30 major liver resections/year). Benchmark values were set at the 75th percentile of median values of all centers. Minimum follow-up period was 1 year in each patient. RESULTS: Of 8044 patients, 2908 (36%) qualified as benchmark (low-risk) cases. Benchmark cutoffs for all indications include R0 resection ≥78%; liver failure (grade B/C) ≤10%; bile leak (grade B/C) ≤18%; complications ≥grade 3 and CCI ® ≤46% and ≤9 at 3 months, respectively. Benchmark values differed significantly between malignant and benign conditions so that reference values must be adjusted accordingly. Extended right hepatectomy (H1, 4-8 or H4-8) disclosed a higher cutoff for liver failure, while extended left (H1-5,8 or H2-5,8) were associated with higher cutoffs for bile leaks, but had superior oncologic outcomes, when compared to formal left hepatectomy (H1-4 or H2-4). The minimal follow-up for a conclusive outcome evaluation following open anatomic major resection must be 3 months. CONCLUSION: These new benchmark cutoffs for open major hepatectomy provide a powerful tool to convincingly evaluate other approaches including parenchymal-sparing procedures, laparoscopic/robotic approaches, and alternative treatments, such as ablation therapy, irradiation, or novel chemotherapy regimens