Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting

Machine learning algorithms, when applied to sensitive data, pose a distinct threat to privacy. A growing body of prior work demonstrates that models produced by these algorithms may leak specific private information in the training data to an attacker, either through the models' structure or their observable behavior. However, the underlying cause of this privacy risk is not well understood beyond a handful of anecdotal accounts that suggest overfitting and influence might play a role. This paper examines the effect that overfitting and influence have on the ability of an attacker to learn information about the training data from machine learning models, either through training set membership inference or attribute inference attacks. Using both formal and empirical analyses, we illustrate a clear relationship between these factors and the privacy risk that arises in several popular machine learning algorithms. We find that overfitting is sufficient to allow an attacker to perform membership inference and, when the target attribute meets certain conditions about its influence, attribute inference attacks. Interestingly, our formal analysis also shows that overfitting is not necessary for these attacks and begins to shed light on what other factors may be in play. Finally, we explore the connection between membership inference and attribute inference, showing that there are deep connections between the two that lead to effective new attacks

ZKBoo: Faster Zero-Knowledge for Boolean Circuits

In this paper we describe ZKBoo, a proposal for practically efficient zero-knowledge arguments especially tailored for Boolean circuits and report on a proof-of-concept implementation. As an highlight, we can generate (resp. verify) a non-interactive proof for the SHA-1 circuit in approximately 13ms (resp. 5ms), with a proof size of 444KB. Our techniques are based on the “MPC-in-the-head” approach to zero-knowledge of Ishai et al. (IKOS), which has been successfully used to achieve significant asymptotic improvements. Our contributions include: 1) A thorough analysis of the different variants of IKOS, which highlights their pro and cons for practically relevant soundness parameters; 2) A generalization and simplification of their approach, which leads to faster Sigma-protocols (that can be made non-interactive using the Fiat-Shamir heuristic) for statements of the form “I know x such that y = f(x)” (where f is a circuit and y a public value); 3) A case study, where we provide explicit protocols, implementations and benchmarking of zero-knowledge protocols for the SHA-1 and SHA-256 circuits

Security of Linear Secret-Sharing Schemes against Mass Surveillance

Following the line of work presented recently by Bellare, Paterson and Rogaway, we formalize and investigate the resistance of linear secret-sharing schemes to mass surveillance. This primitive is widely used to design IT systems in the modern computer world, and often it is implemented by a proprietary code that the provider (“big brother”) could manipulate to covertly violate the privacy of the users (by implementing Algorithm-Substitution Attacks or ASAs). First, we formalize the security notion that expresses the goal of big brother and prove that for any linear secret-sharing scheme there exists an undetectable subversion of it that efficiently allows surveillance. Second, we formalize the security notion that assures that a sharing scheme is secure against ASAs and construct the first sharing scheme that meets this notion. This work could serve as an important building block towards constructing systems secure against mass surveillance

Privacy-Preserving Ridge Regression on Distributed Data

Linear regression is an important statistical tool that models the relationship between some explanatory values and an outcome value using a linear function. In many current applications (e.g. predictive modelling in personalized healthcare), these values represent sensitive data owned by several different parties that are unwilling to share them. In this setting, training a linear regression model becomes challenging and needs specific cryptographic solutions. In this work, we propose a new system that can train a linear regression model with 2-norm regularization (i.e. ridge regression) on a dataset obtained by merging a finite number of private datasets. Our system is composed of two phases: The first one is based on a simple homomorphic encryption scheme and takes care of securely merging the private datasets. The second phase is a new ad-hoc two-party protocol that computes a ridge regression model solving a linear system where all coefficients are encrypted. The efficiency of our system is evaluated both on synthetically generated and real-world datasets

Assessment of clinical and radiological response to sorafenib in hepatocellular carcinoma patients

Sorafenib is an effective anti-angiogenic treatment for hepatocellular carcinoma (HCC). The assessment of tumor progression in patients treated with sorafenib is crucial to help identify potentially-resistant patients, avoiding unnecessary toxicities. Traditional methods to assess tumor progression are based on variations in tumor size and provide unreliable results in patients treated with sorafenib. New methods to assess tumor progression such as the modified Response Evaluation Criteria in Solid Tumors or European Association for the Study of Liver criteria are based on imaging to measure the vascularization and tumor volume (viable or necrotic). These however fail especially when the tumor response results in irregular development of necrotic tissue. Newer assessment techniques focus on the evaluation of tumor volume, density or perfusion. Perfusion computed tomography and Dynamic Contrast-Enhanced-UltraSound can measure the vascularization of HCC lesions and help predict tumor response to anti-angiogenic therapies. Mean Transit Time is a possible predictive biomarker to measure tumor response. Volumetric techniques are reliable, reproducible and time-efficient and can help measure minimal changes in viable tumor or necrotic tissue, allowing the prompt identification of non-responders. Volume ratio may be a reproducible biomarker for tumor response. Larger trials are needed to confirm the use of these techniques in the prediction of response to sorafenib

Transarterial radioembolization for hepatocellular carcinoma: An update and perspectives

In the last decade trans-arterial radioembolization has given promising results in the treatment of patients with intermediate or advanced stage hepatocellular carcinoma (HCC), both in terms of disease control and tolerability profile. This technique consists of the selective intra-arterial administration of microspheres loaded with a radioactive compound (usually Yttrium90), and exerts its therapeutic effect through the radiation carried by these microspheres. A careful and meticulous selection of patients is crucial before performing the radioembolization to correctly perform the procedure and reduce the incidence of complications. Radioembolization is a technically complex and expensive technique, which has only recently entered clinical practice and is supported by scant results from phase III clinical trials. Nevertheless, it may represent a valid alternative to transarterial chemoembolization (TACE) in the treatment of intermediate-stage HCC patients, as shown by a comparative retrospective assessment that reported a longer time to progression, but not of overall survival, and a more favorable safety profile for radioembolization. In addition, this treatment has reported a higher percentage of tumor shrinkage, if compared to TACE, for pre-transplant downsizing and it represents a promising therapeutic option in patients with large extent of disease and insufficient residual liver volume who are not immediately eligible for surgery. Radioembolization might also be a suitable companion to sorafenib in advanced HCC or it can be used as a potential alternative to this treatment in patients who are not responding or do not tolerate sorafenib

Association of Upfront Peptide Receptor Radionuclide Therapy With Progression-Free Survival Among Patients With Enteropancreatic Neuroendocrine Tumors

open57noIMPORTANCE Data about the optimal timing for the initiation of peptide receptor radionuclide therapy (PRRT) for advanced, well-differentiated enteropancreatic neuroendocrine tumors are lacking. OBJECTIVE To evaluate the association of upfront PRRT vs upfront chemotherapy or targeted therapy with progression-free survival (PFS) among patients with advanced enteropancreatic neuroendocrine tumors who experienced disease progression after treatment with somatostatin analogues (SSAs). DESIGN, SETTING, AND PARTICIPANTS This retrospective, multicenter cohort study analyzed the clinical records from 25 Italian oncology centers for patients aged 18 years or older who had unresectable, locally advanced or metastatic, well-differentiated, grades 1 to 3 enteropancreatic neuroendocrine tumors and received either PRRT or chemotherapy or targeted therapy after experiencing disease progression after treatment with SSAs between January 24, 2000, and July 1, 2020. Propensity score matching was done to minimize the selection bias. EXPOSURES Upfront PRRT or upfront chemotherapy or targeted therapy. MAIN OUTCOMES AND MEASURES The main outcome was the difference in PFS among patients who received upfront PRRT vs among those who received upfront chemotherapy or targeted therapy. A secondary outcome was the difference in overall survival between these groups. Hazard ratios (HRs) were fitted in a multivariable Cox proportional hazards regression model to adjust for relevant factors associated with PFS and were corrected for interaction with these factors. RESULTS Of 508 evaluated patients (mean ([SD] age, 55.7 [0.5] years; 278 [54.7%] were male), 329 (64.8%) received upfront PRRT and 179 (35.2%) received upfront chemotherapy or targeted therapy. The matched group included 222 patients (124 [55.9%] male; mean [SD] age, 56.1 [0.8] years), with 111 in each treatment group. Median PFS was longer in the PRRT group than in the chemotherapy or targeted therapy group in the unmatched (2.5 years [95%CI, 2.3-3.0 years] vs 0.7 years [95%CI, 0.5-1.0 years]; HR, 0.35 [95%CI, 0.28-0.44; P < .001]) and matched (2.2 years [95% CI, 1.8-2.8 years] vs 0.6 years [95%CI, 0.4-1.0 years]; HR, 0.37 [95%CI, 0.27-0.51; P < .001]) populations. No significant differences were shown in median overall survival between the PRRT and chemotherapy or targeted therapy groups in the unmatched (12.0 years [95%CI, 10.7-14.1 years] vs 11.6 years [95%CI, 9.1-13.4 years]; HR, 0.81 [95%CI, 0.62-1.06; P = .11]) and matched (12.2 years [95% CI, 9.1-14.2 years] vs 11.5 years [95%CI, 9.2-17.9 years]; HR, 0.83 [95%CI, 0.56-1.24; P = .36]) populations. The use of upfront PRRT was independently associated with improved PFS (HR, 0.37; 95%CI, 0.26-0.51; P < .001) in multivariable analysis. After adjustment of values for interaction, upfront PRRT was associated with longer PFS regardless of tumor functional status (functioning: adjusted HR [aHR], 0.39 [95%CI, 0.27-0.57]; nonfunctioning: aHR, 0.29 [95%CI, 0.16-0.56]), grade of 1 to 2 (grade 1: aHR, 0.21 [95%CI, 0.12-0.34]; grade 2: aHR, 0.52 [95%CI, 0.29-0.73]), and site of tumor origin (pancreatic: aHR, 0.41 [95%CI, 0.24-0.61]; intestinal: aHR, 0.19 [95%CI, 0.11-0.43]) (P < .001 for all). Conversely, the advantage was not retained in grade 3 tumors (aHR, 0.31; 95%CI, 0.12-1.37; P = .13) or in tumors with a Ki-67 proliferation index greater than 10% (aHR, 0.73; 95%CI, 0.29-1.43; P = .31). CONCLUSIONS AND RELEVANCE In this cohort study, treatment with upfront PRRT in patients with enteropancreatic neuroendocrine tumors who had experienced disease progression with SSA treatment was associated with significantly improved survival outcomes compared with upfront chemotherapy or targeted therapy. Further research is needed to investigate the correct strategy, timing, and optimal specific sequence of these therapeutic options.openPusceddu, Sara; Prinzi, Natalie; Tafuto, Salvatore; Ibrahim, Toni; Filice, Angelina; Brizzi, Maria Pia; Panzuto, Francesco; Baldari, Sergio; Grana, Chiara M.; Campana, Davide; Davì, Maria Vittoria; Giuffrida, Dario; Zatelli, Maria Chiara; Partelli, Stefano; Razzore, Paola; Marconcini, Riccardo; Massironi, Sara; Gelsomino, Fabio; Faggiano, Antongiulio; Giannetta, Elisa; Bajetta, Emilio; Grimaldi, Franco; Cives, Mauro; Cirillo, Fernando; Perfetti, Vittorio; Corti, Francesca; Ricci, Claudio; Giacomelli, Luca; Porcu, Luca; Di Maio, Massimo; Seregni, Ettore; Maccauro, Marco; Lastoria, Secondo; Bongiovanni, Alberto; Versari, Annibale; Persano, Irene; Rinzivillo, Maria; Pignata, Salvatore Antonio; Rocca, Paola Anna; Lamberti, Giuseppe; Cingarlini, Sara; Puliafito, Ivana; Ambrosio, Maria Rosaria; Zanata, Isabella; Bracigliano, Alessandra; Severi, Stefano; Spada, Francesca; Andreasi, Valentina; Modica, Roberta; Scalorbi, Federica; Milione, Massimo; Sabella, Giovanna; Coppa, Jorgelina; Casadei, Riccardo; Di Bartolomeo, Maria; Falconi, Massimo; de Braud, FilippoPusceddu, Sara; Prinzi, Natalie; Tafuto, Salvatore; Ibrahim, Toni; Filice, Angelina; Brizzi, Maria Pia; Panzuto, Francesco; Baldari, Sergio; Grana, Chiara M.; Campana, Davide; Davì, Maria Vittoria; Giuffrida, Dario; Zatelli, Maria Chiara; Partelli, Stefano; Razzore, Paola; Marconcini, Riccardo; Massironi, Sara; Gelsomino, Fabio; Faggiano, Antongiulio; Giannetta, Elisa; Bajetta, Emilio; Grimaldi, Franco; Cives, Mauro; Cirillo, Fernando; Perfetti, Vittorio; Corti, Francesca; Ricci, Claudio; Giacomelli, Luca; Porcu, Luca; Di Maio, Massimo; Seregni, Ettore; Maccauro, Marco; Lastoria, Secondo; Bongiovanni, Alberto; Versari, Annibale; Persano, Irene; Rinzivillo, Maria; Pignata, Salvatore Antonio; Rocca, Paola Anna; Lamberti, Giuseppe; Cingarlini, Sara; Puliafito, Ivana; Ambrosio, Maria Rosaria; Zanata, Isabella; Bracigliano, Alessandra; Severi, Stefano; Spada, Francesca; Andreasi, Valentina; Modica, Roberta; Scalorbi, Federica; Milione, Massimo; Sabella, Giovanna; Coppa, Jorgelina; Casadei, Riccardo; Di Bartolomeo, Maria; Falconi, Massimo; de Braud, Filipp