Efficient Privacy-Preserving Matrix Factorization via Fully Homomorphic Encryption

Recommendation systems become popular in our daily life. It is well known that the more the release of users’ personal data, the better the quality of recommendation. However, such services raise serious privacy concerns for users. In this paper, focusing on matrix factorization-based recommendation systems, we propose the first privacy-preserving matrix factorization using fully homomorphic encryption. On inputs of encrypted users\u27 ratings, our protocol performs matrix factorization over the encrypted data and returns encrypted outputs so that the recommendation system knows nothing on rating values and resulting user/item profiles. It provides a way to obfuscate the number and list of items a user rated without harming the accuracy of recommendation, and additionally protects recommender\u27s tuning parameters for business benefit and allows the recommender to optimize the parameters for quality of service. To overcome performance degradation caused by the use of fully homomorphic encryption, we introduce a novel data structure to perform computations over encrypted vectors, which are essential operations for matrix factorization, through secure 2-party computation in part. With the data structure, the proposed protocol requires dozens of times less computation cost over those of previous works. Our experiments on a personal computer with 3.4 GHz 6-cores 64 GB RAM show that the proposed protocol runs in 1.5 minutes per iteration. It is more efficient than Nikolaenko et al.\u27s work proposed in CCS 2013, in which it took about 170 minutes on two servers with 1.9 GHz 16-cores 128 GB RAM

Instant Privacy-Preserving Biometric Authentication for Hamming Distance

In recent years, there has been enormous research attention in privacy-preserving biometric authentication, which enables a user to verify him or herself to a server without disclosing raw biometric information. Since biometrics is irrevocable when exposed, it is very important to protect its privacy. In IEEE TIFS 2018, Zhou and Ren proposed a privacy-preserving user-centric biometric authentication scheme named PassBio, where the end-users encrypt their own templates, and the authentication server never sees the raw templates during the authentication phase. In their approach, it takes about 1 second to encrypt and compare 2000-bit templates based on Hamming distance on a laptop. However, this result is still far from practice because the size of templates used in commercialized products is much larger: according to NIST IREX IX report of 2018 which analyzed 46 iris recognition algorithms, size of their templates varies from 4,632-bit (579-byte) to 145,832-bit (18,229-byte). In this paper, we propose a new privacy-preserving user-centric biometric authentication (HDM-PPBA) based on Hamming distance, which shows a big improvement in efficiency to the previous works. It is based on our new single-key function-hiding inner product encryption, which encrypts and computes the Hamming distance of 145,832-bit binary in about 0.3 seconds on Intel Core i5 2.9GHz CPU. We show that it satisfies simulation-based security under the hardness assumption of Learning with Errors (LWE) problem. The storage requirements, bandwidth and time complexity of HDM-PPBA depend linearly on the bit-length of biometrics, and it is applicable to any large templates used in NIST IREX IX report with high efficiency

Validation of quick sequential organ failure assessment score for poor outcome prediction among emergency department patients with suspected infection

Objective The quick sequential organ failure assessment (qSOFA) score, which includes mentation, systolic blood pressure, and respiratory rate, was developed to identify serious sepsis in out-of-hospital or emergency department (ED) settings. We evaluated the ability of the qSOFA score to predict poor outcome in South Korean ED patients with suspected infection. Methods The qSOFA score was calculated for adult ED patients with suspected infection. Patients who received intravenous or oral antibiotics in the ED were considered to have infection. In-hospital mortality rate, admission rate, intensive care unit (ICU) admission rate, length of hospital stay (LOS), and lactate levels were compared between the qSOFA score groups. Receiver operating characteristic curves and area under the receiver operating characteristic curve values for in-hospital mortality were calculated according to qSOFA cut-off points and lactate levels. Results Of 2,698 patients, in-hospital mortality occurred in 134 (5.0%). The mortality rate increased with increasing qSOFA score (2.2%, 6.4%, 17.5%, and 42.4% for qSOFA scores 0, 1, 2, and 3, respectively, P<0.001). The admission rate, ICU admission rate, LOS, and lactate level also increased with increasing qSOFA score (all P<0.001). The area under the receiver operating characteristic curve values for predicting in-hospital mortality associated with qSOFA score, lactate ≥2 mmol/L, and lactate ≥4 mmol/L were 0.719 (95% confidence interval [CI], 0.670 to 0.768), 0.657 (95% CI, 0.603 to 0.710), and 0.632 (95% CI, 0.571 to 0.693), respectively. Conclusion Patients with a higher qSOFA score had higher admission, ICU admission, and in-hospital mortality rates, longer LOS, and higher lactate level. The qSOFA score showed better performance for predicting poor outcome than lactate level

Privacy enhanced matrix factorization for recommendation with local differential privacy

Recommender systems are collecting and analyzing user data to provide better user experience. However, several privacy concerns have been raised when a recommender knows user's set of items or their ratings. A number of solutions have been suggested to improve privacy of legacy recommender systems, but the existing solutions in the literature can protect either items or ratings only. In this paper, we propose a recommender system that protects both user's items and ratings. We develop novel matrix factorization algorithms under local differential privacy (LDP). In a recommender system with LDP, individual users randomize their data themselves to satisfy differential privacy and send the perturbed data to the recommender. Then, the recommender computes aggregates of the perturbed data. This framework ensures that both user's items and ratings remain private from the recommender. However, applying LDP to matrix factorization typically raises utility issues with high dimensionality and iterative algorithms. To tackle these technical challenges, we adopt dimensionality reduction technique and a sampling-based binary mechanism. We introduce a factor that stabilizes the perturbed gradients. With MovieLens and LibimSeTi datasets, we evaluate accuracy of our recommender system and demonstrate that our algorithm performs better than the existing differentially private gradient descent algorithm for matrix factorization under stronger privacy requirements.MOE (Min. of Education, S’pore

Automated Extraction and Presentation of Data Practices in Privacy Policies

Privacy policies are documents required by law and regulations that notify users of the collection, use, and sharing of their personal information on services or applications. While the extraction of personal data objects and their usage thereon is one of the fundamental steps in their automated analysis, it remains challenging due to the complex policy statements written in legal (vague) language. Prior work is limited by small/generated datasets and manually created rules. We formulate the extraction of fine-grained personal data phrases and the corresponding data collection or sharing practices as a sequence-labeling problem that can be solved by an entity-recognition model. We create a large dataset with 4.1k sentences (97k tokens) and 2.6k annotated fine-grained data practices from 30 real-world privacy policies to train and evaluate neural networks. We present a fully automated system, called PI-Extract, which accurately extracts privacy practices by a neural model and outperforms, by a large margin, strong rule-based baselines. We conduct a user study on the effects of data practice annotation which highlights and describes the data practices extracted by PI-Extract to help users better understand privacy-policy documents. Our experimental evaluation results show that the annotation significantly improves the users’ reading comprehension of policy texts, as indicated by a 26.6% increase in the average total reading score

Lattice-Based Secure Biometric Authentication for Hamming Distance

Biometric authentication is a protocol which verifies a user’s authority by comparing her biometric with the pre-enrolled biometric template stored in the server. Biometric authentication is convenient and reliable; however, it also brings privacy issues since biometric information is irrevocable when exposed. In this paper, we propose a new user-centric secure biometric authentication protocol for Hamming distance. The biometric data is always encrypted so that the verification server learns nothing about biometric information beyond the Hamming distance between enrolled and queried templates. To achieve this, we construct a single-key function-hiding inner product functional encryption for binary strings whose security is based on a variant of the Learning with Errors problem. Our protocol consists of a single round, and is almost optimal in the sense that its time and space complexity grow quasi-linearly with the size of biometric templates. On implementation with concrete parameters, for binary strings of size ranging from 579 to 18,229 bytes (according to NIST IREX IX report), our scheme outperforms previous work from the literature.N

Lattice-Based Secure Biometric Authentication for Hamming Distance

© 2021, Springer Nature Switzerland AG.Biometric authentication is a protocol which verifies a user’s authority by comparing her biometric with the pre-enrolled biometric template stored in the server. Biometric authentication is convenient and reliable; however, it also brings privacy issues since biometric information is irrevocable when exposed. In this paper, we propose a new user-centric secure biometric authentication protocol for Hamming distance. The biometric data is always encrypted so that the verification server learns nothing about biometric information beyond the Hamming distance between enrolled and queried templates. To achieve this, we construct a single-key function-hiding inner product functional encryption for binary strings whose security is based on a variant of the Learning with Errors problem. Our protocol consists of a single round, and is almost optimal in the sense that its time and space complexity grow quasi-linearly with the size of biometric templates. On implementation with concrete parameters, for binary strings of size ranging from 579 to 18,229 bytes (according to NIST IREX IX report), our scheme outperforms previous work from the literature.N

Red Ginseng Dietary Fiber Shows Prebiotic Potential by Modulating Gut Microbiota in Dogs

ABSTRACT Red ginseng, widely used in traditional medicine for various conditions, imparts health benefits mainly by modulating the gut microbiota in humans. Given the similarities in gut microbiota between humans and dogs, red ginseng-derived dietary fiber may have prebiotic potential in dogs; however, its effects on the gut microbiota in dogs remain elusive. This double-blinded, longitudinal study investigated the impact of red ginseng dietary fiber on the gut microbiota and host response in dogs. A total of 40 healthy household dogs were randomly assigned to low-dose (n = 12), high-dose (n = 16), or control (n = 12) groups and fed a normal diet supplemented with red ginseng dietary fiber (3 g/5 kg body weight per day, 8 g/5 kg per day, or no supplement, respectively) for 8 weeks. The gut microbiota of the dogs was analyzed at 4 weeks and 8 weeks using 16S rRNA gene sequencing of fecal samples. Alpha diversity was significantly increased at 8 and 4 weeks in the low-dose and high-dose groups, respectively. Moreover, biomarker analysis showed that short-chain fatty acid producers such as Sarcina and Proteiniclasticum were significantly enriched, while potential pathogens such as Helicobacter were significantly decreased, indicating the increased gut health and pathogen resistance by red ginseng dietary fiber. Microbial network analysis showed that the complexity of microbial interactions was increased by both doses, indicating the increased stability of the gut microbiota. These findings suggest that red ginseng-derived dietary fiber could be used as a prebiotic to modulate gut microbiota and improve gut health in dogs. IMPORTANCE The canine gut microbiota is an attractive model for translational studies, as it responds to dietary interventions similarly to those in humans. Investigating the gut microbiota of household dogs that share the environment with humans can produce highly generalizable and reproducible results owing to their representativeness of the general canine population. This double-blind and longitudinal study investigated the impact of dietary fiber derived from red ginseng on the gut microbiota of household dogs. Red ginseng dietary fiber altered the canine gut microbiota by increasing diversity, enriching short-chain fatty acid-producing microbes, decreasing potential pathogens, and increasing the complexity of microbial interactions. These findings indicate that red ginseng-derived dietary fiber may promote canine gut health by modulating gut microbiota, suggesting the possibility of its use as a potential prebiotic